Cover page
MARYAM DANESHFAR
Integration of geospatial and
environmental data to support
building renovation
Doctoral Dissertation
DEPARTMENT OF CIVIL SYSTEMS ENGINEERING
TECHNISCHE UNIVERSITÄT BERLIN
Title page
Integration of geospatial and environmental data to support
building renovation
vorgelegt von
Maryam Daneshfar, M. Sc.
ORCID: 0000-0001-6234-0359
an der Fakultät VI – Planen Bauen Umwelt
der Technischen Universität Berlin
zur Erlangung des akademischen Grades
Doktor der Ingenieurwissenschaften
- Dr.-Ing. -
genehmigte Dissertation
Promotionsausschuss:
Vorsitzender: Prof. Dr. Dietmar Stephan
Gutachter: Prof. Dr. Timo Hartmann
Gutachter: Prof. Dr. James O’Donnell
Gutachter: Prof. Dr. Karsten Menzel
Tag der wissenschaftlichen Aussprache: 10. Mai 2024
Berlin 2024
III
Acknowledgment
I would like to express my deepest gratitude to Prof. Dr. Timo Hartmann for his supervision, continuous
support, and invaluable insights throughout this research journey. His guidance and encouragement have
been pivotal in shaping this research.
I am also thankful to Prof. Jochen Rabe for trusting in me and giving me the chance to contribute to the
BIM-SPEED project, where I could develop my research. I acknowledge the support of Technische
Universität Berlin and BIM-SPEED consortium for providing me the resources and environment for the
successful completion of this thesis.
I thank my colleagues and friends in Civil System Engineering group for all the valuable discussions,
brainstorming, relaxing lunch breaks and friendly chats.
Last but not least, my sincere appreciation goes to my family for their love and support. I reserve my
warmest gratitude for Mahdi, whose endless and unconditional love and support have been a constant
source of strength.
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V
Summary
Building renovation can present more complexities compared to constructing new buildings due to the
necessity of dealing with existing conditions. This is not only limited to the building as-is condition but
also encompasses the urban, environmental, and societal context of the building. A key goal of building
renovation is to improve the performance of the building and comfort of inhabitants. By taking into
account the complexity of the urban context and integrating it in the building modeling process, it is
possible to achieve more accurate building simulations, which leads to a more realistic assessment of
the building’s performance and comfort.
A prerequisite to determining the important external features is having a thorough understanding of the
related concepts in the external domain. The primary objective of this dissertation is to develop such a
knowledge framework to be used as a frame of reference for the experts and engineers involved in
renovation projects. Building within an urban context is surrounded and influenced by various urban
features, including neighboring buildings, roads, vegetation, and water bodies. These features are
represented and characterized within the geospatial domain. Various data standards and schemes have
been developed to represent this domain. However, these representations are not directly applicable to
building renovation since they lack a focus on the required or beneficial concepts for this task and fail
in incorporating expert knowledge in developing the model. Hence, this dissertation proposes a
knowledge framework in the form of an ontology to represent the required and beneficial concepts of
geospatial domain to support building renovation activities. The proposed knowledge framework is built
upon the knowledge captured from previous studies that implicitly mention the effect of such datasets
in building renovation. Also expert knowledge is integrated through workshops and brainstorming, and
the concepts are identified based on specific tasks and for particular use cases within renovation projects.
The applicability of this knowledge framework is demonstrated for site planning use case of a residential
building under renovation in Berlin, Germany.
The development of this knowledge framework showed that selection of various weather datasets and
the Inter-Building Effect (IBE) are key factors in building performance. To present the sensitivity of
building performance to such factors and to determine the extent of their impact, the dissertation
explores various alternatives through analytical studies and gain insights into the complex interplay
between the building and its environment. This involves systematically analyzing different
configurations, and input variables to assess their influence on the building's behavior and performance.
This approach facilitates a more comprehensive and realistic assessment of building performance,
leading to enhanced design decisions and improved occupant comfort.
In this regard, in the second stage of the research, to explore the sensitivity of building performance to
the selection of weather data, various typical-year weather datasets retrieved from different sources and
generated from different periods and methodologies are applied in the energy simulation of three
residential buildings in Europe. The research reflects on the range of disparities caused by these
VI
alternatives. Based on that, it highlights the effect of such parameters and calls on decision-makers in
renovation projects to meticulously investigate the influence of their weather data selection. The
research also highlights the impact of policymaking. It suggests that standardization organizations
should develop new approaches for developing weather datasets that represent long-term climate
conditions more realistically.
The last stage of the research focuses on the sensitivity of building performance to IBE, through studying
the shading effect of surrounding buildings on the building performance of renovation projects. The
research expands simplified urban typologies by generating randomly selected heights and distances of
surrounding buildings in a nine-block network. Following that, the impact of such typologies are
investgated on the thermal demand and illuminance level of various residential buildings in Europe. The
research reflects on the range of disparities caused by each urban layout and emphasizes the effect of
such input variables on building thermal and visual performance. The study also highlights the need to
thoroughly investigate the impact of such data on the decision-making process of renovation scenarios
and the possibility of integrating specific materials, shading devices, and so on in the renovation
workflow. It also proposes a workflow for IBE integration to the renovation roadmaps. Studying the
weather data selection and IBE highlights that renovation projects should consider the future possible
alternatives of the built environment to compensate for the renovation costs.
Therefore, the main contributions of this research can be summarized as follows: 1) a knowledge
framework in the form of an ontology to represent concepts in the geospatial domain to support energy
efficiency of building renovation; 2) two explorative analyses to investigate the sensitivity of building
performance to a) various weather datasets, b) various complex urban typologies. The dissertation also
discusses the role of policymaking and standardization in effectively including such variables in
renovation projects.
VII
Zusammenfassung
Die Renovierung von Gebäuden kann im Vergleich zum Bau neuer Gebäude komplexer sein,
da man sich mit bestehenden Bedingungen auseinandersetzen muss. Dies beschränkt sich nicht
nur auf den Ist-Zustand des Gebäudes, sondern umfasst auch den städtischen, ökologischen und
gesellschaftlichen Kontext des Gebäudes. Ein Hauptziel von Gebäuderenovierungen ist die
Verbesserung der Leistungsfähigkeit des Gebäudes und des Komforts der Bewohner. Durch die
Berücksichtigung der Komplexität des städtischen Kontexts und dessen Integration in den
Gebäudemodellierungsprozess ist es möglich, genauere Gebäudesimulationen durchzuführen,
was zu einer realistischeren Bewertung der Gebäudeleistung und des Komforts führt.
Eine Voraussetzung für die Bestimmung wichtiger äußerer Merkmale ist ein gründliches
Verständnis der damit verbundenen Konzepte im Außenbereich. Das Hauptziel dieser
Dissertation ist die Entwicklung von wissensbasierten Rahmenbedingungen, der als Referenz
für die an Renovierungsprojekten beteiligten Experten und Ingenieure dienen soll. Gebäude in
einem städtischen Kontext sind von verschiedenen städtischen Merkmalen umgeben und
werden von diesen beeinflusst, einschließlich benachbarter Gebäude, Straßen, Vegetation und
Gewässern. Diese Merkmale werden im Bereich der Geodaten dargestellt und charakterisiert.
Es wurden zudem verschiedene Datenstandards und Schemata entwickelt, um diesen Bereich
darzustellen. Diese Darstellungen sind jedoch nicht direkt auf Gebäuderenovierungen
anwendbar, da sie sich nicht auf die für diese Aufgabe erforderlichen oder nützlichen Konzepte
konzentrieren und Expertenwissen nicht in die Entwicklung des Modells einbeziehen. Daher
wird in dieser Dissertation wissensbasierte Rahmenbedingungen in Form einer Ontologie
dargestellt, um die erforderlichen und nützlichen Konzepte des georäumlichen Bereichs zur
Unterstützung von Gebäuderenovierungsaktivitäten darzustellen. Die vorgeschlagenen
Rahmenbedingungen bauen auf dem Wissen aus früheren Studien auf, die implizit die Wirkung
solcher Datensätze bei der Gebäuderenovierung erwähnen. Auch Expertenwissen wird anhand
durchgeführter Workshops und Brainstorming integriert, und die Konzepte werden basierend
auf spezifischen Aufgaben und für bestimmte Anwendungsfälle innerhalb von
Renovierungsprojekten identifiziert. Die Anwendbarkeit dieser Rahmenbedingungen wird für
den Anwendungsfall der Standortplanung eines zu renovierenden Wohngebäudes in Berlin,
Deutschland, demonstriert.
Die Entwicklung der Rahmenbedingungen zeigte, dass die Auswahl verschiedener Wetterdaten
und der Inter-Building-Effekt (IBE) Schlüsselfaktoren für die Gebäudeleistung sind. Um die
Empfindlichkeit der Gebäudeleistung gegenüber solchen Faktoren darzustellen und das
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Ausmaß ihrer Auswirkungen zu bestimmen, werden in der Dissertation verschiedene
Alternativen durch analytische Studien untersucht und Einblicke in das komplexe
Zusammenspiel zwischen dem Gebäude und seiner Umgebung gewonnen. Dazu werden
verschiedene Konfigurationen und Eingangsgrößen systematisch analysiert, um ihren Einfluss
auf das Verhalten und die Leistung des Gebäudes zu bewerten. Dieser Ansatz ermöglicht eine
umfassendere und realistischere Bewertung der Gebäudeleistung, was zu besseren
Planungsentscheidungen und höherem Nutzerkomfort führt.
In diesem Zusammenhang werden in der zweiten Phase der Forschung zur Untersuchung der
Empfindlichkeit der Gebäudeleistung gegenüber der Auswahl der Wetterdaten verschiedene
Wetterdatensätze für ein typisches Jahr, die aus unterschiedlichen Quellen stammen und in
verschiedenen Zeiträumen und mit unterschiedlichen Methoden erzeugt wurden, in der
Energiesimulation von drei Wohngebäuden in Europa angewendet. In der Untersuchung wird
die Bandbreite der durch diese Alternativen verursachten Unterschiede untersucht. Auf dieser
Grundlage werden die Auswirkungen solcher Parameter hervorgehoben und die
Entscheidungsträger bei Renovierungsprojekten aufgefordert, den Einfluss der von ihnen
gewählten Wetterdaten genau zu untersuchen. Die Untersuchung zeigt auch den Einfluss der
Politik auf. Sie schlägt vor, dass Normungsorganisationen neue Ansätze für die Entwicklung
von Wetterdatensätzen entwickeln sollten, die langfristige Klimabedingungen realistischer
darstellen.
Die letzte Phase der Forschung konzentriert sich auf die Empfindlichkeit der Gebäudeleistung
gegenüber IBE, indem der Verschattungseffekt der umliegenden Gebäude auf die
Gebäudeleistung von Renovierungsprojekten untersucht wird. Die Forschung erweitert
vereinfachte städtische Typologien, indem sie zufällig ausgewählte Höhen und Abstände der
umliegenden Gebäude in einem Netz aus neun Blöcken erzeugt. Anschließend werden die
Auswirkungen solcher Typologien auf den Wärmebedarf und die Beleuchtungsstärke von
verschiedenen Wohngebäuden in Europa untersucht. Die Studie reflektiert die Bandbreite der
Unterschiede, die durch das jeweilige Stadtlayout verursacht werden, und unterstreicht die
Auswirkungen solcher Eingangsgrößen auf die thermische und visuelle Leistung von
Gebäuden. Die Studie unterstreicht auch die Notwendigkeit, die Auswirkungen solcher Daten
auf den Entscheidungsprozess von Renovierungsszenarien und die Möglichkeit der Integration
spezifischer Materialien, Beschattungsvorrichtungen usw. in den Renovierungsablauf
gründlich zu untersuchen. Sie schlägt auch einen Arbeitsablauf für die Integration von IBE in
die Renovierungspläne vor. Die Untersuchung der Wetterdatenauswahl und der IBE zeigt, dass
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Renovierungsprojekte die möglichen zukünftigen Alternativen der gebauten Umwelt
berücksichtigen sollten, um die Renovierungskosten zu kompensieren.
Daher lassen sich die Hauptbeiträge dieser Forschung wie folgt zusammenfassen: 1) die
Zusammenstellung wissensbasierter Rahmenbedingungen in Form einer Ontologie zur
Darstellung von Konzepten im Geodatenbereich zur Unterstützung der Energieeffizienz von
Gebäudesanierungen; 2) zwei explorative Analysen zur Untersuchung der Sensitivität der
Gebäudeleistung in Bezug auf a) verschiedene Wetterdatensätze und b) verschiedene komplexe
städtische Typologien. Die Dissertation erörtert auch die Rolle der Politik und der
Standardisierung bei der effektiven Einbeziehung solcher Variablen in Renovierungsprojekte.
X
XI
Table of Contents
Summary ..................................................................................................................................... V
Zusammenfassung ................................................................................................................... VII
List of Figures ........................................................................................................................ XIII
List of Tables .......................................................................................................................... XIV
List of Abbreviations ............................................................................................................... XV
1. Introduction......................................................................................................................... 1
1.1. Background and frame of reference ............................................................................ 1
1.2. Motivation and research gap........................................................................................ 2
1.2.1. A knowledge framework to represent geospatial data ................................................................... 5
1.2.2. Sensitivity of building performance to the exterior conditions ...................................................... 5
1.3. Research questions ...................................................................................................... 7
1.4. Research Approach ...................................................................................................... 8
1.4.1. Developing a knowledge framework to represent the geospatial data ........................................... 8
1.4.2. Sensitivity of building performance simulation to exterior conditions .......................................... 9
1.4.3. Case studies .................................................................................................................................. 11
1.5. Structure of the thesis ................................................................................................ 14
1.6. References ................................................................................................................. 16
2. An Ontology to Represent Geospatial Data to Support Building Renovation .................. 17
2.1. Abstract ...................................................................................................................... 17
2.2. Introduction ............................................................................................................... 18
2.3. Research Background and Motivation ...................................................................... 19
2.3.1. Geospatial and Environmental Data in Building Renovation ...................................................... 19
2.3.2. Ontologies in Geospatial Domain ................................................................................................ 20
2.3.3. Identifying Research Gap and Contribution ................................................................................ 21
2.4. Methodology .............................................................................................................. 21
2.4.1. Literature Review ......................................................................................................................... 22
2.4.2. Ontology Development ................................................................................................................ 22
2.4.3. Ontology Evaluation .................................................................................................................... 24
2.5. Results ....................................................................................................................... 26
2.5.1. Knowledge Capture from Literature Review ............................................................................... 26
2.5.2. An Ontology to Represent Surrounding Environment of a Building ........................................... 28
2.5.3. Ontology Verification .................................................................................................................. 30
2.5.4. Ontology Validation ..................................................................................................................... 31
2.6. Discussion .................................................................................................................. 34
2.7. Conclusion ................................................................................................................. 36
Acknowledgement ................................................................................................................ 37
2.8. References ................................................................................................................. 38
3. Is It Fundamental to Examine the Weather Data for a Reliable Building Energy
Simulation? A Comparative Study with Different Weather Datasets ...................................... 43
3.1. Abstract ...................................................................................................................... 43
3.2. Introduction ............................................................................................................... 44
XII
3.3. Research Background and Motivation ...................................................................... 45
3.3.1. Weather data in building energy simulation ................................................................................ 46
3.3.2. Building energy simulation using different weather datasets: previous studies .......................... 48
3.3.3. Research gaps and contributions .................................................................................................. 50
3.4. Methodology .............................................................................................................. 51
3.4.1. Case studies .................................................................................................................................. 51
3.4.2. Weather data collection ................................................................................................................ 52
3.4.3. Comparison of energy simulation results using different weather datasets ................................. 54
3.5. Result ......................................................................................................................... 54
3.6. Discussion .................................................................................................................. 57
3.7. Conclusion ................................................................................................................. 60
Acknowledgement ................................................................................................................ 60
3.8. References ................................................................................................................. 61
4. The Inter-Building Effect (IBE) in Evaluating Building Performance of Renovation
Projects: The Case of European Cities .................................................................................... 65
4.1. Abstract ...................................................................................................................... 65
4.2. Introduction ............................................................................................................... 66
4.3. Literature Review ...................................................................................................... 67
4.3.1. Research gaps and contributions .................................................................................................. 70
4.4. Method and Material ................................................................................................. 71
4.4.1. Building models ........................................................................................................................... 72
4.4.2. Calculating the Inter-Building Effect (IBE) ................................................................................. 74
4.5. Result ......................................................................................................................... 77
4.6. Discussion .................................................................................................................. 79
4.7. Conclusion ................................................................................................................. 83
Acknowledgments ................................................................................................................ 83
4.8. References ................................................................................................................. 84
5. Discussion and Conclusion ............................................................................................... 87
5.1. Reflections ................................................................................................................. 89
5.2. Limitations and future research ................................................................................. 91
5.3. Endnote ...................................................................................................................... 93
5.4. References ................................................................................................................. 95
Appendix A: Candidate’s contribution and co-authorship ...................................................... 97
Appendix B: The OWL file of the ontology developed in Protégé. .......................................... 98
XIII
List of Figures
Figure 1.1. Decision-making process in renovation projects; own representation adopted from
[2]. .............................................................................................................................................. 2
Figure 1.2. Input parameters for BPS and the scope of study in this thesis presented in red-
dashed lines. ............................................................................................................................... 9
Figure 1.3. Applied case studies in this research, selected across Europe. .............................. 12
Figure 1.4. Berlin case study. ................................................................................................... 12
Figure 1.5. Gdynia case study. ................................................................................................. 13
Figure 1.6. Vitoria-Gasteiz case study. .................................................................................... 13
Figure 1.7. Contribution of the dissertation in connection with the publications. ................... 14
Figure 2.1. Intersection of urban ontology and CityGML schema. ......................................... 21
Figure 2.2. Procedure of developing the ontology. .................................................................. 22
Figure 2.3. Ontology evaluation effort in summary. ................................................................ 24
Figure 2.4. Object view in the proposed ontology. .................................................................. 29
Figure 2.5. Process view in the proposed ontology.................................................................. 30
Figure 2.6. Result of the faCT++ reasoner. .............................................................................. 31
Figure 2.7. The prototype implemented based on the ontology. .............................................. 32
Figure 2.8. Maps of the surrounding data for Berlin renovation case study. ........................... 34
Figure 3.1. The workflow of the study. .................................................................................... 51
Figure 3.2. 3D Model of the case studies (left to right: Berlin, Gdynia, and Vitoria). ............ 52
Figure 3.3. Annual heat demand using different weather datasets. .......................................... 55
Figure 3.4. Monthly heat demand using different weather datasets......................................... 56
Figure 4.1. Urban form measures - authors’ representation adopted from [16], [18]. ............. 68
Figure 4.2. Methodology of the research. ................................................................................ 72
Figure 4.3. Placement of daylight control (left to right: Gdynia, Berlin). ............................... 73
Figure 4.4. Daylight evaluation spaces in the case studies left to right Gdynia and Berlin case
studies. ...................................................................................................................................... 73
Figure 4.5. Real urban context of the case studies (left to right: Gdynia, Berlin). .................. 75
Figure 4.6. Pavilion and slab urban typologies (left to right: Gdynia, Berlin)......................... 75
Figure 4.7. The building block representing Table 4.5. ........................................................... 77
Figure 4.8. The building block representing Table 4.6. ........................................................... 77
Figure 4.9. Proposed workflow for IBE integration in renovation projects. ............................ 81
Figure 5.1. The path through the dissertation........................................................................... 87
XIV
List of Tables
Table 1.1. State of the art and contribution of the dissertation. ................................................. 4
Table 2.1. Ontology Specification............................................................................................ 23
Table 2.2. Recommendations and modifications from verification workshop. ....................... 30
Table 2.3. Comments from the experts in validation workshop. ............................................. 32
Table 3.1. A description of weather datasets (EnergyPlus, PVGIS, MEREEN weather
service). .................................................................................................................................... 54
Table 3.2. Percentage change ratio for annual and monthly energy consumption using
different weather datasets compared to average energy consumption. .................................... 57
Table 4.1. Selected studies of IBE in building energy performance. ....................................... 68
Table 4.2. Window glass characteristic of the buildings.......................................................... 73
Table 4.3. Window-wall characteristic of buildings (North: (315 to 45 deg), East: (45 to 135
deg), South: (135 to 225 deg), West: (225 to 315 deg))........................................................... 74
Table 4.4. Range of heights and distances for generating random typologies. ........................ 75
Table 4.5. Hypothetical typology with varying heights and distances of surrounding buildings
(case study in Gdynia). ............................................................................................................. 76
Table 4.6. Hypothetical typology with varying heights and distances of surrounding buildings
(case study in Berlin)................................................................................................................ 76
Table 4.7. IBE on monthly and annual heating demand considering various typologies
compared to stand-alone building – Gdynia case study. .......................................................... 77
Table 4.8. IBE on monthly and annual heating demand considering various typologies
compared to stand-alone building – Berlin case study. ............................................................ 78
Table 4.9. IBE on daylight considering various typologies compared to stand-alone building –
Room space in the Gdynia case study. ..................................................................................... 78
Table 4.10. IBE on daylight considering various typologies compared to stand-alone building
– Kitchen space in the Gdynia case study. ............................................................................... 79
Table 4.11. IBE on daylight considering various typologies compared to stand-alone building
– Living room space in the Berlin case study. ......................................................................... 79
Table 4.12. IBE on daylight considering various typologies compared to stand-alone building
– Bedroom space in the Berlin case study................................................................................ 79
XV
List of Abbreviations
ADE Application Domain Extension
AEC Architecture, Engineering, and Construction
ASHRAE American Society of Heating, Refrigerating, and Air-
Conditioning Engineers
BIM Building Information Modeling
BEM Building Energy Modeling
BPS Building Performance Simulation
COSMO-REA Consortium for Small-scale Modeling Re-Analysis
DFavg Average Daylight Factor
EC European Commission
ECMWF ERA-5 European Centre for Medium-Range Weather Forecasts Re-
Analysis-5
EPW Energy Plus Weather Format
EU European Union
FS Finkelstein-Schafer
gbXML Green Building XML
GCM Global Climate Model
GIS Geographic Information System
GML Geography Markup Language
HVAC Heating, Ventilation, and Air Conditioning
IBE Inter-Building Effect
IFC Industry Foundation Classes
IMGW Meteorologii i Hosomaki Wodnej
ISH Integrated Surface Hourly weather data
ISO International Organization for Standardization
IWEC International Weather for Energy Calculations
LULC Land Use, and Land Cover
MERA2 NASA Modern-Era Retrospective Analysis-2 National Aeronautics and
Space Administration
MEREEN MEteo REelle pour la simulation Energetique (EN: MEteo REal
for ENergy simulation)
NREL National Renewable Energy Laboratory
OGC Open Geospatial Consortium
PVGIS Photovoltaic Geographic Information System
RCM Regional Climate Model
SOA Service-Oriented Architecture
SSA Sky Solid Angle
SW Synthetic Weather
SWEC Spanish Weather for Energy Calculations
SYNOP/METAR CAMS Surface SYnoptic Observations/METeorological Aviation
Report Copernicus Atmosphere Monitoring Service
TMY Typical Meteorological Year
TRY Test Reference Year
WFS Web Feature Service
WYEC Weather Year for Energy Calculation
XML Extensible Markup Language
XVI
General
etc. et cetera
i.e. id est
e.g. exempli gratia
et al. et alii
1
1. Introduction
1.1. Background and frame of reference
The EU plan for 2030 is a 32% improvement in energy efficiency compared to 1990 [1]. A key aspect
of this goal focuses on increasing the energy efficiency of buildings through renovation, as buildings
account for 40% of energy consumption in the EU. In addition to addressing the energy crisis, climate
emergency calls for more efficient and resilient buildings to alleviate current and upcoming challenges.
Buildings are dynamic systems made up of interconnected elements and exhibit patterns of behavior
over time. These systems can be impacted by both internal and external forces. Surrounding geospatial
and environmental data are examples of external factors. In current research and policy, the
neighborhood approach in renovation wave means the communication of buildings in terms of energy
use and production, for instance, through renewable systems, to provide the required energy for a target
building [1]. However, there are other perspectives in considering neighborhood in renovation projects,
particularly in the planning and design phase, which has not been discussed adequately.
In theory, researchers suggest that building renovation is an interdisciplinary activity. Therefore, for
informed decision-making, a comprehensive preliminary study is required that includes information
about the building characteristics, such as its layout, number of floors, and opening, and the urban
context, including location, landscape quality, accessibility, and historical value [2]. However, currently,
there is a lack of a comprehensive knowledge framework for representing and incorporating such
concepts (related to the urban context and outdoor environmental conditions) throughout different stages
of renovation. In addition, the influence of such parameters is not investigated extensively in renovation
case studies of European cities.
In practice, a common approach in renovation is to collect data only at the individual building level,
which can lead to neglecting the impact of exterior factors in the decision-making process of renovation
scenarios. This occurs often because engineers and designers are unaware of the potential of external
factors on the accuracy of their analyses in various stages of renovation. The decision-making process
for particular goals within renovation projects typically involves evaluating the as-built condition of the
building, followed by assessing the performance of various design alternatives (Figure 1.1). The
accuracy of both stages influences the outcome of the renovation project, while both stages are affected
by external factors.
I
2
Figure 1.1. Decision-making process in renovation projects; own representation adopted from [2].
This dissertation aims to draw engineers, energy experts, and decision-makers’ attention to the
importance of geospatial and environmental data in renovation projects. The first part of the thesis
focuses on developing a knowledge framework to aid AEC (Architecture, Engineering and
Construction) domain experts in managing the necessary geospatial data for energy-efficient building
renovation within the ubiquitous proliferation of these datasets for cities. The second part of the thesis
builds upon this knowledge and investigates the effect of incorporating real-life complexities into
building performance simulations in individual building renovation. Through analytical studies, the
dissertation provides insights about the sensitivity of Building Performance Simulation (BPS) to the
selection of various weather datasets and the inter-building effect (IBE) by studying the shading effect
of surrounding buildings. By posing what-if questions, the dissertation aims to help understand the
building’s potential reaction to future changes [3]. The following section provides more details about
the motivation of the thesis and the research gaps addressed.
1.2. Motivation and research gap
Generally, in construction projects, surrounding geospatial features are integrated into a building model
through BIM (Building Information Modeling) and GIS (Geographic Information System) integration.
The main applications of BIM-GIS integration in developing a sustainable built environment include
energy management, urban governance, and the life-cycle of AEC projects, including design, planning,
construction, operation, and maintenance [4]. BIM, as a detailed object-oriented parametric information
model representing a building in 3D, helps in indoor planning tasks, while GIS helps facilitate the
outdoor planning tasks such as spatial analysis, network analysis, and calculating the distance between
points [5]. Most studies on BIM and GIS data integration focus on the technical aspects of
interoperability for integration. For renovation projects, this topic is seldom discussed. One reason is
that BIM is typically used for new construction rather than for existing buildings [5].
Besides the technical aspects of data integration, which are imperative, a vital preliminary step is
identifying the essential geospatial and environmental concepts required for integration and evaluating
the impact of such concepts on building performance to support building renovation. To approach this
topic, this dissertation considers two perspectives namely conceptual and practical. The challenges that
motivate this dissertation are summarized below:
3
• There is currently no comprehensive knowledge framework available to represent the geospatial data
required or beneficial for different use cases in renovation projects. Such a framework is a pre-
requisite for understanding the potential of such data in renovation projects for designers and energy
experts, who are often not aware of the availability and potential of such data.
• The climate issue as one of such concepts requires more investigation in renovation projects to ensure
that renovation design tackles future challenges. There are two main reasons for this need: 1) climate
change is a recent phenomenon and requires more scrutiny with more recent weather datasets; 2) the
heat wave in European cities has become increasingly critical in recent years and is a topic related to
the health of occupants, particularly vulnerable groups, such as the elderly and children [1].
Therefore, a key question in renovation projects is how sensitive building performance simulation is
to the selection of various weather datasets, particularly concerning climate change effects.
• The heat wave in European cities affects not only the heating and cooling demand through
temperature but also the thermal and visual comfort of the inhabitants through varying solar gain.
Building solar gain is influenced by building orientation and shading effect of surrounding obstacles
such as buildings in the immediate proximity of the target building. Therefore, another key question
in building renovation is how sensitive building performance simulation is to the shading effect of
different placement of surrounding buildings to consider for future urban developments.
To tackle these challenges from a conceptual point of view, the research focuses on developing an
ontology to facilitate clear communication among various expert groups of renovation projects. This
ontology aims to provide a structured framework that enhances understanding and collaboration across
various disciplines. A knowledge framework to represent such concepts and relations is currently
missing in renovation projects. Such a knowledge framework can be also applied as a frame of reference
for the experts in renovation projects to communicate about the effect of such data in practice. In the
initial phase of a renovation project, design decisions significantly influence the overall building energy
performance through geometry, orientation, window-to-wall ratio, shading installations, and so on [7].
A comprehensive view of the building’s site can help designers make more efficient decisions without
having the physical prototype of the system. While the main objective here is to generate such a
knowledge framework, it is important to acknowledge that interoperability between the data of building
and geospatial domain is critical, which has been covered by other research.
To approach the topic from a practical point of view and to realize the significance of integration of
geospatial and environmental data into renovation processes, the dissertation focuses on how sensitive
building performance simulation is to such external parameters. Deep renovation in Europe aims to
significantly reduce building energy consumption by maximizing potential energy savings, thereby
enhancing building energy performance [7]. Integrating more real-life complexities, such as surrounding
urban features and environmental conditions, into the building performance simulation helps move
4
toward deep renovation. Among the features in the urban context, surrounding buildings affect the
building’s energy and performance due to their shading and microclimate effect. Previous studies
investigated the shading of various simplified urban typologies in building performance. However, more
in-depth investigation about the effect of complex urban layouts, particularly in renovation projects is
still required. Another important exterior factor is the weather data, integrated into building energy and
performance simulation directly as input. Although the sensitivity of building performance to variation
of various weather datasets is investigated in previous studies, it is still ongoing research due to the
nature of this dataset, which is recurring and dramatically affected by climate change.
Table 1.1. State of the art and contribution of the dissertation.
State of the art
Contribution of current
research
Conceptual perspective
Knowledge farmwork as a
basis for data integration
• No frame of reference is available
to represent geospatial concepts
that can support building
renovation.
• Existing geospatial models are
generic.
• No expert knowledge is integrated
in development of such data models
to specifically support renovation.
• Developing an ontology that
represents an explicit list of
geospatial concepts
required/beneficial for various
pre-defined use cases.
• Integrated domain expert
knowledge in development of
the ontology.
Practical perspective
Sensitivity of building performance simulation to:
Selection of weather data
• Typical year weather data
generated from historical recent
periods have not been studied in
Europe extensively enough.
• Existing research do not consider
case studies from different climate
zones of Europe.
• Existing research do not focus on
renovation case studies.
• Comparative analysis on
building performance simulation
results using various typical year
weather datasets generated from
various periods including recent
ones.
• Using various renovation case
studies in different climate
zones of Europe.
Shading effect of
surrounding buildings
• Very limited studies in European
cities and climate.
• Real urban context or simplified
urban typologies has been studied.
• Existing research on this topic aims
at supporting urban planning not
building renovation.
• Investigating effect of complex
urban typologies.
• Renovation case studies in
European cities.
• Aimed at supporting renovation
of individual buildings.
Table 1.1 summarizes the state of the art and the contribution of the dissertation, and the following
sections describe the contribution of this thesis in more detail, including a knowledge framework which
5
represents geospatial data required for building renovation, as well as sensitivity of building
performance to specific exterior conditions.
1.2.1. A knowledge framework to represent geospatial data
As mentioned, despite all the technical deficiencies in the integration of geospatial data into the building
model, i.e., interoperability, geometry incompatibility, and semantics, the possibility for such integration
exists with various software. However, in the context of building renovation, a gap exists at the
conceptual level, and that is the absence of a knowledge framework that identifies the concepts from
geospatial domain, suitable for integration, while accounting for the particularities of renovation
projects. A key challenge in developing this knowledge framework is that, existing geospatial data
models and standards are not tailored to specific applications and do not involve domain expert
knowledge. For geospatial data integration in construction projects, such as renovation, it is critical to
integrate the knowledge of practitioners and engineers so that the model represents the required concepts
and their attributes. Thus, the first study of this thesis attempts to fill this gap by developing an ontology-
based knowledge framework that incorporates engineers’ knowledge through workshops and interviews.
This knowledge framework contributes to the field of Advanced Engineering Informatics, by
formalizing the complex engineering knowledge of geospatial domain. As the main goal, this explicit
representation of knowledge supports individuals or groups of engineers in interpreting solutions of
intermediate stages [8]. In the next step, it can help engineers solve computational problems in BIM and
GIS data integration. The framework is intended to evolve as knowledge grows and changes [8].
1.2.2. Sensitivity of building performance to the exterior conditions
Engineers design and analyze complex systems to solve societies’ most critical problems and improve
people’s quality of life [9]. The main goal of renovation projects is to increase the building’s
performance and energy efficiency. Building performance analysis and energy modeling helps to study
the energy consumption of a building and identify alternatives to improve its performance [10]. Similar
to any model, the accuracy of outputs in building energy simulation depends on the accuracy of input
[11], which can be defined based on the application of the model. While including a real-life condition
can provide more accurate results, a simplified hypothetical model can remove complications and
provide control over analysis [12]. Generally, a set of minimum input parameters are a prerequisite for
simulation, but additional parameters help improve the accuracy. For renovation projects, usually, it is
not sufficient to solely assess building energy performance based on the building envelope [13].
In the conventional building energy simulation, the model inputs include building parameters such as
geometry, openings, material, HVAC, occupancy schedule, and the weather data as an external factor.
The inclusion of geospatial features in the immediate surroundings of the building is not a prerequisite
6
for simulation. However, many studies investigated the effect of these parameters in terms of shading,
micro-climate, cooling, and ventilation effects. The shading effect of nearby buildings can change the
solar gain on the building façade, and the daylight received through windows. This thesis investigates
two external factors, namely the weather data and the shading effect of surrounding buildings. Both
topics have been studied expensively. Nevertheless, this thesis attempts to shed light on the existing
gaps and provide insights by extending the body of knowledge with a focus on renovation projects. The
complexity in each of the mentioned factors is described in the following.
1.2.2.1. Weather data
A significant amount of research is focused on the effects of climate on building performance simulation
because of the urgency of climate change. Detailed description of these studies is provided in Section
3.3.2. The complexity comes from the fact that weather data used in building energy simulations should
represent long-term climate conditions covering the life span of a building. Typical weather datasets are
developed to meet this expectation, but there are challenges associated with these datasets, such as the
sources of data, the methods used to develop the typical weather data, the parameters used in the method,
and the weight assigned to each parameter to generate the weather dataset. On the other hand, the
weather datasets utilized in building simulation are not tailored to meet the specific requirements of
building performance analysis [14].
Although such information is scrutinized by researchers in various studies, engineers, energy experts,
and practitioners do not empirically consider them in practice. When engineers and decision-makers are
informed about the effects of such elements, they can make well-grounded decisions to control cost and
quality in renovation projects.
1.2.2.2. Surrounding buildings and their shading effect
Surrounding buildings, trees, and other obstacles affect the solar gain, building energy consumption and
daylight of the building due to their shading effects [15]. Typologies are used to categorize various types
or patterns of urban areas, including building networks, among other factors. Martin and March’s
typologies are three main simplified building block networks representing a target building in the middle
of a nine-block network [16]. Prior research has examined the performance of the building in these
typologies. Detailed description of these studies is provided in Section 4.3. However, the gap lies in
studying the building performance in more complex urban layouts and typologies to address the real-
world intricacies. As highlighted by Aksamija [6], it is important to consider such studies at the
schematic level, as this information can help in designing different envelope design options evaluating
various shading systems and solar access.
7
The following section elaborates on the research questions of this dissertation based on the described
motivation and the identified research gaps.
1.3. Research questions
The main research question motivating this dissertation is how integration of surrounding geospatial
and environmental data can support building renovation. To answer this question, three complementary
questions are formulated as below:
RQ1. How can an ontology-based knowledge framework, representing concepts from geospatial
domain, support building renovation?
In response to the need for a knowledge framework that represents geospatial concepts that support
building renovation, an ontology has been developed. This ontology is sought to support all stages of a
renovation project, from planning to procurement and maintenance. This ontological approach attempts
to bridge the gap between the building domain and the geospatial domain to facilitate a common
understanding of geospatial domain intricacies in building renovation. The goal is to offer a systematic
approach for the experts in renovation projects to communicate about geospatial data and to support
them in the decision-making of renovation scenarios to enhance the overall output of the renovation
practice. Integration of domain expert knowledge into the ontology has been performed via validation
workshops and brainstorming sessions.
RQ2. What are the implications of using different typical-year weather datasets for building energy
performance analysis in the context of building renovation, and how can this knowledge be used to
inform the development of better weather standards and policies?
As weather data is one of the important external input parameters in building energy modeling, it is
crucial to investigate the implications of using various weather datasets on building performance
simulations. Therefore, the research endeavors to shed light on potential discrepancies in predicted
energy performance and consumption due to employing different typical-year weather datasets as
representatives of long-term climate conditions compared to the average energy performance of the
building. By such an exploratory analysis, the study seeks to offer insights to help move toward more
precise building performance simulations in renovation projects. As a result, it suggests developing more
context-specific weather standards and policies.
RQ3. What are the implications of considering the shading effect of surrounding buildings on building
energy performance simulation, and how can this knowledge be used to allow better building design in
renovation projects?
8
While the shading effect of various simplified urban typologies has been investigated in previous
studies, this research aims at delving into exploring more complex typologies and the discrepancies they
cause in predicted energy consumption and daylight gain of the buildings, compared to the stand-alone
building. By such an explorative analysis, the research offers insights to integrate such information into
renovation decision-making procedures, which can lead to strategic building design.
The motivation behind research questions 2 and 3 is to inform designers and engineers about the
variations of building performance simulation results due to the integration of various external datasets.
Such insight is essential for improving the decision-making process in designing a high-performance
building [17]. The next section describes the research approach applied in this dissertation.
1.4. Research Approach
This section summarizes the research approach of the dissertation, comprising two main sections. The
first section includes the methodology used for developing the knowledge framework in the form of an
ontology and evaluating it. The second section describes the method for building performance
simulation and its variations due to integrating surrounding buildings and various weather datasets.
1.4.1. Developing a knowledge framework to represent the geospatial data
The thesis describes how to develop a logical model that represents the geospatial domain, focusing on
concepts in the urban domain and for the specific task of building renovation. Knowledge engineering
suggests ontology and logic for building computational models with specific applications and for a
particular engineering purpose [9]. Ontology is a formal representation of physical and abstract concepts
and their relations. For this research, the scope is limited to the urban domain and the concepts that can
support the task of building renovation. To identify the relevant concepts, the involvement of experts
and practitioners is crucial [9]. Through interviews and workshops with practitioners and engineers
involved in renovation projects and ontology development, a thorough literature review of the research
selected by snowballing technique, and applying the methodologies for developing an ontology by
choosing the context, scope, user, and end-use, the research identifies the relevant use cases and
addresses the concepts from geospatial domain which are beneficial in such use cases in building
renovation projects.
The ontology includes an explicit list of physical and abstract concepts, represented in object and process
components, inspired by the CityGML data model [18]. Verifying and validating the ontology, as an
important step in the ontology development process, is addressed through open-ended experimental
workshops for targeted purposes and consistency checking using a reasoner. Based on the ontology, a
prototype has been implemented and used to evaluate if the ontology is competent for the intended uses.
Using the prototype, the developed ontology is instantiated for a case study in Europe and is used to
9
validate the ontology for the site planning use case. The next section describes the approach of the
second and third phase of the dissertation.
1.4.2. Sensitivity of building performance simulation to exterior conditions
Three main approaches are applied for building performance modeling, namely, white box, black box,
and grey box [19]. White box modeling is based on the physics of the building, black box modeling is
based on the existing historical performance of the building and grey box modeling is a hybrid method
that combines both approaches. Traditional white-box energy modeling requires several parameters for
a thorough calculation of the energy consumption and performance of the building [19]. For
simplification, this approach focuses on analyzing an individual building as a single system [20]. Energy
experts and planners are not usually aware of the available data on the urban context and its potential
for building renovation. Therefore, a cross-scale, context-sensitive modeling method is required to show
its capacity to change the result of the building performance [20]. Understanding the sensitivity of
building performance to these elements helps determine the level of detail required about the building
and the urban context according to the objective of the simulation [14].
Therefore, the next stage of this thesis focuses on the sensitivity of building performance simulation by
developing a comparative analysis using various models. The intended simulation output are heat energy
consumption and daylight of the building. Two experimental setups are created, one with various
weather datasets and the other with varying urban layouts to study the shading effect. The next two
sections describe the setups for generating the experiments for the comparative analysis, and Figure 1.2
demonstrates the scope of this thesis for studying these effects in the overall view of the topic.
Figure 1.2. Input parameters for BPS and the scope of study in this thesis presented in red-dashed lines.
10
1.4.2.1. Weather data
Selecting weather data depends on its intended use in building simulation. For assessing buildings under
‘typical’ long-term conditions, typical-year weather data can be used. However, there is a challenge in
that, such weather data is generated from various periods of historical actual weather data, and data for
different parameters may come from various sources. The method used to generate typical-year weather
data is to select the most typical months, but there is no universal scheme for implementing it. The
statistical approach to generate typical months is based on cumulative probability distribution for the
minimum, the maximum, and the mean of weather parameters, such as temperature, humidity, solar
radiation. However, each typical weather datum may focus on different weather parameters and use
different weights for composing the data and generating a typical weather dataset, representing the area’s
climate. Therefore, using typical-year weather data in simulations may not be robust for calculating peak
load and building performance under extreme conditions. However, it is proven to be reasonably
accurate for predicting long-term energy consumption [21].
In this thesis various typical-year weather datasets generated from different methodologies and varying
periods of historical actual weather data are selected. A comparative study is performed on the building
performance in terms of heating demand using typical-year weather data generated from very recent
actual weather data (years 2006-2015), typical-year weather data generated from years before 2000, and
actual weather data of years 2006-2015, to calculate average energy use of the corresponding years. In
addition, a morphing strategy is suggested to generate synthetic weather data to represent the area’s
climate more realistically.
Simulations are performed on three residential buildings representing typical structures of selected cities
located in various climate geo-clusters of Europe. The weather datasets are collected from various
sources, including EnergyPlus weather database [22], PVGIS [23], and MEREEN weather service [24].
Based on the results, the discrepancies in building performance prediction caused by selected weather
datasets are discussed.
1.4.2.2. Shading effect of surrounding buildings
In the second experimental setup, the effect of various urban layouts on building performance is
investigated. Previous studies either only consider the stand-alone building, the existing real urban
context or a simplified typology based on Martin and March’s three main typologies, namely, pavilion,
slab, and courtyard [16], which place neighboring buildings at the same distance from the target building
with same heights. The research argues that future typological settings can differ from the existing ones,
or the simplified layouts considered in previous studies. Therefore, to generate more hypothetical
complex typologies, distances and heights of surrounding buildings are selected randomly from a range
of values. Building performance, including heat demand and daylight capture, has been simulated for
11
the building within these complex layouts, and the results have been compared with the performance of
the stand-alone building.
In general, the daylight simulation process requires information about various parameters, including the
scene geometry and surrounding landscape, the viewpoint, building type, lighting requirements, and
schedule of inhabitants and sky model. The sky model can vary based on the time of the simulation, the
location of the building (its coordinates on the global coordinate system), sky conditions (such as cloud
cover), and weather data (including solar radiation data). In addition, depending on the application,
various details about the surrounding landscape, such as facade material, are required. After completing
the daylight model based on the mentioned information, the simulation engine calculates the global
illumination within a scene. The simulation engine used in this research is Splitflux, and the sky model
is the Perez illuminance model, both integrated into EnergyPlus software [25]. The Spliflux method
bases its calculations on the objects that are in direct view of the daylight sensor. The Perez illuminance
model is based on the clearness and brightness of the sky.
1.4.3. Case studies
The above concepts and findings are directly applied and demonstrated in real renovation cases across
Europe [26]. The aim of using these case studies is to evaluate an existing building's performance with
all its characteristics, such as material and age, concerning its surrounding context, the typicality of
urban development of the city in which it is located, and its climate zone. In Europe, there are five
climate geo-clusters: southern, north-western, central, eastern, Mediterranean, and western. Figure 1.3
shows the three case studies used in the simulation studies of this thesis. In general, the three case studies
represent three climate zones: north-western (Berlin, Germany), central (Gdynia, Poland), and southern
(Vitoria-Gasteiz, Spain). All the buildings are residential and are located in the urban contexts that
represent typical urban areas of their region. The building models include detailed information such as
geometry, material, and glazing, which are described in detail in the next chapters in the respective
studies.
12
Figure 1.3. Applied case studies in this research, selected across Europe.
1.4.3.1. Case study 1: Berlin, Germany
The case study in Berlin (Figure 1.4), built in 1960, is a residential apartment block (consisting of two
separate structures) located in Lichtenrade (south of Berlin). It has a 3574.3 m2 area, 56 dwellings, six
floors, a basement, and a ground floor. The material used in the current condition of the building is
reinforced concrete with insulation. The dwelling requires deep renovation in the envelope and HVAC.
The building is close to a road and railway, which makes the acoustic insulation of the building
necessary. On the other hand, the building is not in a high-density area. However, in low proximity to
the building, there are various rows of trees, which can affect the building's shading analysis,
microclimate, noise, and air quality investigation caused by the road.
Figure 1.4. Berlin case study.
Geo-cluster
Central
Europe
Geo-cluster
North-
Western
Europe
Geo-
cluster
Southern
Europe
13
1.4.3.2. Case study 2: Gdynia, Poland
The case study in Gdynia (Figure 1.5) is a duplex dwelling with 153.43 m2 area, built in 1961. The
façade of the building is brick and plaster, and expanded polystyrene is used for insulation. Deep
renovation in building envelope and HVAC systems is expected in this case study, as well. The building
is located within a mid-density urban area.
Figure 1.5. Gdynia case study.
1.4.3.3. Case study 3: Vitoria-Gasteiz, Spain
The case study in Vitoria (Figure 1.6) is a group of 10 multi-family residential apartment blocks in four
stories in an area of 838.76 m2, which are built between 1940 and 1970. The building has a U-shape, a
garage and a bar in the ground floor. Reinforced concrete is used as the main structure and the roof,
while no insulation is applied in the building, which led to poor energy performance and humidity issues
in the building. Hence, deep renovation on building envelopes and HVAC systems is required, with
plans for integration into the district heating system. The building is located in a very dense urban area,
which represent a typical old town in this region. Wind circulation and change of temperature within
this urban context may change the urban microclimate. The tight adjacency of buildings influences the
thermal and visual performance of the building.
Figure 1.6. Vitoria-Gasteiz case study.
14
1.5. Structure of the thesis
This section summarizes the main contribution of this thesis based on the problem statement and
research approach described in the previous sections. The dissertation is written in a cumulative format,
comprising five chapters in total. Chapters 2, 3 and 4 correspond to the published and submitted papers
answering the research questions (Figure 1.7). The format of the publications was adjusted to fit the
format of the dissertation. In the following a summary of the chapters 2-4 is provided.
Figure 1.7. Contribution of the dissertation in connection with the publications.
Chapter 2 is a response to RQ1, which is about developing a knowledge framework to represent
geospatial data that can support building renovation. This chapter presents the developed ontology that
maps surrounding geospatial concepts for different renovation tasks and use cases within building
renovation. This study demonstrates that ontology is a good approach for such representation and
highlights the critical significance of integrating domain experts, i.e., engineers and energy experts
involved in renovation studies, to reflect on the developed ontology as the main users of this knowledge
framework. This research contributes to the body of knowledge by generating a common framework for
the surrounding data required in building renovation. It has an implication in practice for engineers by
providing a shared knowledge framework and for software developers by providing a basis for BIM and
GIS data integration for renovation purposes.
Chapter 3 answers the second research question. RQ2 focuses on the potential discrepancies in building
performance prediction caused by selecting various weather datasets. To answer this question, different
typical-year weather datasets generated from different periods of record are applied in estimating the
building performance in terms of heating demand. Then, the simulation results are compared with the
average annual and monthly heating demand of buildings in different climate geo-clusters of Europe, as
explained in Section 1.4.2 of this chapter.
The findings agree with the previous studies that higher values for over/underestimation of building
energy demand should be expected when employing different typical-year weather datasets. The chapter
15
concludes that modifying typical-year weather data with actual average dry-bulb temperature may
increase the representativeness of the weather data for calculating average energy demand. Finally, the
paper demonstrates that it is fundamental to carefully select the appropriate weather dataset for specific
applications and locations, considering its correctness, completeness, and representativeness. The article
also emphasizes that standardization organizations should envisage new standards for weather data
compatible with climate zones and climate change effects for building energy simulations.
Chapter 4 investigates RQ3, which is about the sensitivity of building performance to the shading effect
of surrounding buildings. The study extends the existing simplified urban typologies by considering
randomly selected heights and distances of surrounding buildings in a nine-block network. It evaluates
the heating demand and daylight (illuminance level) in specific zones of two residential buildings in
Gdynia and Berlin. The research findings demonstrate that the IBE can significantly affect simulation
outcomes, highlighting the importance of considering current and future urban developments in
renovation projects. Based on the study's findings, this research proposes a workflow that can help
integrate the IBE into renovation projects, ensuring that the urban developments in future are adequately
considered. This study highlights the importance of considering the impact of the immediate built
environment on the energy demand and daylight availability in buildings, which can result in better
decision-making in renovation projects.
Lastly, Chapter 5 concludes the dissertation by discussing and reflecting on the findings, limitations,
and recommendations for further research.
16
1.6. References
[1] European Commission, “Communication from the Commission to the European Parliament, the
Council, the European Economic and Social Committee and the Committee of the Regions,”
2011.
[2] M. Grecchi, Building Renovation: How to Retrofit and Reuse Existing Buildings to Save Energy
and Respond to New Needs. Springer Nature, 2022.
[3] D. Wright and D. H. Meadows, Thinking in systems. 2008.
[4] H. Wang, Y. Pan, and X. Luo, “Integration of BIM and GIS in sustainable built environment: A
review and bibliometric analysis,” Autom Constr, vol. 103, pp. 41–52, 2019.
[5] W. W. A. Basir, Z. Majid, U. Ujang, and A. Chong, “Integration of GIS and BIM techniques in
construction project management–A review,” The International Archives of Photogrammetry,
Remote Sensing and Spatial Information Sciences, vol. 42, pp. 307–316, 2018.
[6] A. Aksamija, “A strategy for energy performance analysis at the early design stage: predicted vs.
Actual building energy performance,” Journal of Green Building, vol. 10, no. 3, pp. 161–176,
2015.
[7] BPIE (Buildings Performance Institute Europe), “Deep Renovation: Shifting from exception to
standard practice in EU Policy.,” 2021.
[8] J. Kunz, I. Smith, and T. Tomiyama, “Editorial.,” Advanced Engineering Informatics , vol. 16,
no. 1, 2002.
[9] T. Hartmann and A. Trappey, “Advanced Engineering Informatics - Philosophical and
methodological foundations with examples from civil and construction engineering,”
Developments in the Built Environment, p. 100020, 2020, doi: 10.1016/j.dibe.2020.100020.
[10] N. N. A. Bakar et al., “Energy efficiency index as an indicator for measuring building energy
performance: A review. Renewable and Sustainable Energy Reviews, ,” vol. 44, pp. 1–11, 2015.
[11] S. Labi, Introduction to civil engineering systems: A systems perspective to the development of
civil engineering facilities . John Wiley & Sons, 2014.
[12] M. Oh and Y. Kim, “Identifying urban geometric types as energy performance patterns,” Energy
for Sustainable Development, vol. 48, pp. 115–129, 2019.
[13] J. He, A. Hoyano, and T. Asawa, “A numerical simulation tool for predicting the impact of
outdoor thermal environment on building energy performance,” Applied energy, vol. 86, no. 9,
pp. 1596–1605, 2009.
[14] I. Beausoleil-Morrison, Fundamentals of Building Performance Simulation. Routledge, 2020.
[15] A. G. Valarakos, V. Karkaletsis, D. Alexopoulou, E. Papadimitriou, C. D. Spyropoulos, and G.
Vouros, “Building an allergens ontology and maintaining it using machine learning techniques,”
Comput Biol Med, vol. 36, no. 10, pp. 1155–1184, 2006.
[16] L. Martin, L. A. Martin, and L. March, Urban space and structures. Cambridge University Press,
1972.
[17] Ajla Aksamija and Abul Abdullah, “Building Technology Research in Architectural Practice:
Lessons Learned from Implementations of Energy-Efficient Advanced Building Technologies,”
in Proceedings of ACEEE 2013 Summer Study on Energy Efficiency in Industry, 2013.
[18] T. H. Kolbe, “CityGML, KML und das Open Geospatial Consortium,” 2008.
[19] Z. Zhang, “BIM to GIS-based building model conversion in support of urban energy simulation.
,” Lund University GEM thesis series., 2018.
[20] M. S. Al-Homoud, “Computer-aided building energy analysis techniques. ,” Build Environ, vol.
36, no. 4, pp. 421–433, 2001.
[21] Charles Barnaby and Drury Crawley, “Building Performance Simulation for Design and
Operation, chapter Weather and climate in building performance simulation,” Routledge, 2019.
[22] “Weather Data Sources.” Accessed: Oct. 03, 2023. [Online]. Available:
https://energyplus.net/weather/sources
[23] “PVGIS, Photovoltaic Geographical Information System, Joint Research Centre.” Accessed:
Sep. 28, 2022. [Online]. Available: http://re.jrc.ec.europa.eu/pvgis.html
[24] “MEREEN.” [Online]. Available: https://mereen.dimn-cstb.fr/
[25] “EnergyPlusTM Version 22.1.0 Documentation Engineering Reference,” 2022.
[26] “BIM-Speed EU Horizon 2020 Project.” [Online]. Available: https://www.bim-speed.eu/en
17
2. An Ontology to Represent Geospatial Data to
Support Building Renovation
(Status: Published) Daneshfar, M., Hartmann, T., Rabe, J.; An ontology to represent
geospatial data to support building renovation.
2.1. Abstract
Energy-efficient building renovation is an inter-disciplinary task and requires investigation about the
building condition in the urban, environmental, and societal context. Existing literature implicitly
mentions the effect of surrounding data in different stages of building renovation. Nevertheless, no
conceptual framework is available for practitioners to realize the potential of such data in specific phases
of the renovation. The main goal of this study is to understand: (1) based on what knowledge framework
surrounding geospatial and environmental data can support building renovation projects, (2) if
developing an ontology can help representing this knowledge framework, and (3) how experts and
engineers involved in the renovation process can contribute to development of this knowledge
framework. The results present an ontology that maps surrounding geospatial and environmental
concepts for different renovation tasks and use cases within building renovation. The ontology is built
upon knowledge captured from previous studies that implicitly mention the effect of these datasets in
building renovation, as well as expert knowledge, brainstorming, and monitoring construction sites.
Additionally, a semi-structured verification and validation workshop has been performed to incorporate
insights from experts directly involved in different stages of building renovation process. This paper
contributes to the body of knowledge by generating a common framework for the surrounding data
required in building renovation. It has an implication in practice for engineers by providing a shared
knowledge framework and for software developers by providing a basis for BIM (Building Information
Modeling) and GIS (Geographic Information System) data integration for renovation purposes.
Keywords: building renovation; geospatial data; knowledge framework; ontology.
II
18
2.2. Introduction
A ‘Climate Neutral Europe by 2050’ is one of the actions at the European level, where policies are
targeted toward increasing building renovation rates and depth of energy saving in the renovation
process [1]. Energy-efficient building renovation is an inter-disciplinary task. It needs to cover domains
with different ontological outsets, such as contextual, environmental, and societal data [2]. Investigating
the surrounding geospatial and environmental data can help to highlight the impact of some of these
factors. Experts in the architecture, engineering, and construction (AEC) domain apply BIM and GIS
data integration, as a common practice, to benefit from the geospatial datasets in construction projects.
Sani and Rahman [3] and Zhu et al. [4] have carried out an extensive review of these studies. For building
renovation, Göçer [5] scrutinized the effect of surrounding buildings, vegetation, and parking lots in the
quality of building data collection. In addition, Kamari et al. [2] generated a framework to assess the
building renovation performance. The framework includes datasets from different fields, including the
geospatial domain. Nevertheless, they do not explicitly mention the required surrounding geospatial data
for building renovation.
Today, municipalities devote significant effort in collecting geospatial data for cities. As a result,
voluminous amounts of geospatial data for different locations in several levels of detail are available.
However, searching for geospatial data for a specific application from this pool of data is overwhelming
and requires expertise [6]. Having a framework for managing geospatial datasets and realizing their
potential in different phases of building renovation is missing in existing renovation studies. The first
motivation of this paper is to fill this gap and provide an overview of the required geospatial and
environmental entities to support building renovation.
Ontology is an approach for “an explicit specification of a conceptualization”, and conceptualization is
the way of "thinking about a domain” [7]. There are different targets for developing ontologies. One of
them is supporting engineering design requirement capture to help the experts in the domain
communicate more conveniently [8]. The real-world is a broad topic and modeling and creating an
ontology for such a system is a huge task. A common practice is to model the geospatial data for a
specific application and domain that narrows it down to the required concepts. For instance, urban
ontology prunes the geospatial concepts and relations and keeps those required for urban analysis.
Therefore, the second goal of this research is to investigate whether ontology development helps to
generate this knowledge framework. Creating such a knowledge framework is beneficial since it can be
reused in different renovation cases in various locations [6].
To develop the ontology, the first step is to identify the exact renovation tasks, where geospatial data are
required or beneficial, according to the knowledge retrieved from the previous studies. Based on that,
relevant concepts and relations are determined. The last step is to validate the ontology against the use
cases and within the scope of those specific tasks with the tight involvement of experts and engineers.
19
Hence, the final motivation of this paper is to include experts and engineers involved in the renovation
process into the development of this ontology.
The paper is structured as follows: Section 2.3 presents the research background and motivation. Section
2.4 summarizes the methodology utilized for developing and evaluating the ontology. Section 2.5
presents the results including the ontology implementation and evaluation. Finally, the discussion and
conclusions are provided in Sections 2.6 and 2.7, respectively.
2.3. Research Background and Motivation
2.3.1. Geospatial and Environmental Data in Building Renovation
Geographical data has been represented in the construction domain for different purposes such as urban
planning, emergency response, mobility, and railway planning [9], [10]. Within the context of building
renovation, many studies investigated the effect of surrounding and environmental data in applications
related to renovation tasks such as building energy modeling, accessibility to the renovation site,
acoustic and thermal comfort analysis [11], [12], [13], [14], [15], [16], [17]. To practically integrate
these concepts with building information models, Göçer et al. [5] introduced a pre-retrofit model that
performs a BIM and GIS integration strategy to combine data to provide contextual information about
the building under renovation. However, this study does not enumerate the required contextual datasets.
Costa et al. [18] developed a platform including an integrated ontology-based District Data Model
(DDM). The DDM is a data model which semantically integrates data of building and urban scale
required for retrofitting. The urban data in this ontology includes the geometry of the building envelope,
and the geometric representation of urban elements such as green areas, roads, and city furniture. The
authors do not explicitly mention the required or beneficial data for the application of building
renovation. They believe that these datasets can have an indirect effect on the renovation process. In this
study, the developed platform uses this ontology to collect data in IFC and CityGML format. However,
this study highlights that CityGML cannot help in structuring all the necessary data. Therefore, other
datasets were added to the platform in the form of contextual data [18].
Researchers presume that BIM and GIS data integration is the optimal solution in practice for providing
the data flow between construction and urban domains. However, they usually miss an intermediate
phase in which required concepts should be specified [19]. This necessities development of ontologies
that can cover all essential concepts for a specific task. Applying prevalent data models such as CityGML
for building renovation is not valid since it does not contain all required concepts for different
applications in the building renovation workflow. In addition, expert knowledge required for renovation
is missing to a great extent.
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2.3.2. Ontologies in Geospatial Domain
To model geospatial data, Open Geospatial Consortium (OGC) introduced different standards.
LandInfra is a conceptual model for representing infrastructure facilities such as roads and railways [20].
GML (Geography Markup Language) is an XML (Extensible Markup Language) grammar for
expressing geographical features [21]. IndoorGML is a GML application schema to represent indoor
spatial information [22]. CityGML is an application schema of GML for the 3D representation of data.
CityGML was developed to reach a common definition and understanding of the basic entities,
attributes, and relations within a 3D city model [23]. It is widely used, and recently a growing number
of projects generate CityGML models of different cities. This model is also employed frequently in
integration with IFC (Industry Foundation Classes) in construction projects. IFC is an open international
digital standard description of the built environment. Different parties in a construction project use it for
exchanging information. This model only focuses on the individual building model and does not include
surrounding information [24]. Another building information model is gbXML (Green Building XML),
which aims at facilitating and enabling the interoperability between building design and engineering
analysis, such as energy simulation of the building. The gbXML schema includes building information
required for building energy modeling, such as thermal zones, and some surrounding information, such
as vegetation [25].
It is tempting to use 3D city models such as CityGML for urban applications. However, CityGML
components do not provide sufficient concepts for particular applications [9]. Currently, in the geospatial
domain, there is no representation that suits all applications due to complexity of the domain [26]. Task-
specific ontologies can address this issue [27]. An ontology should be developed within a specific
domain and task that restrict the scope and universe of discourse. Besides, it should be developed in
close collaboration with stakeholders and practitioners in the domain [28].
Spatial data modeling is investigated in different applications from different perspectives. However, to
the author’s knowledge, no study applied an ontology-based approach for the building renovation
application. To narrow down the geospatial domain, we applied urban ontology as the starting point, as
we assume the building under renovation is located in an urban context. Urban ontology categorizes the
urban-related features to objects such as buildings and roads; processes such as population density;
relations such as building block has buildings; and events such as traffic accidents (Figure 2.1). In the
ontology domain, ‘object’ is a term of art that is considered as things, events, and processes of all sorts
[29]. In the context of this research, an object is a ‘spatial thing’ that comprehends boundaries of physical
(such as building and road) or non-physical (such as district and zip code) features. Therefore, it is
analogous to the ‘CityObject’ concept in CityGML.
Thus, in the object view of our ontology, some concepts are inspired by the concepts introduced in
CityObject in CityGML, such as building, vegetation, and water bodies (Figure 2.1). However, sub-
21
categories of these concepts are customized to focus on specific concepts related to renovation
processes.
Figure 2.1. Intersection of urban ontology and CityGML schema.
2.3.3. Identifying Research Gap and Contribution
Based on previous studies mentioned in Sections 2.3.1 and 2.3.2, we identified three research gaps.
Firstly, a knowledge framework for geospatial and environmental data is missing in building renovation
studies. Such a framework helps engineers and experts in the renovation workflow comprehend the
benefit and requirement of geospatial and environmental datasets. However, majority of studies
implicitly mentioned it for specific applications related to building renovation. Therefore, as a first
contribution, we conducted a literature review on these studies and identified the relevant concepts as
basis for a knowledge framework. As a second contribution, we developed an ontology to represent this
knowledge framework. Lastly, we evaluated the ontology with experts and engineers. Hence, in a
bottom-up approach, we integrated the knowledge of renovation project practitioners into the ontology
development. In the next section, we explain the methodology utilized for developing the ontology and
its evaluation.
2.4. Methodology
This section presents the methodology utilized for developing this ontology. As depicted in Figure 2.2,
the process starts with a literature review on the studies which mention the utilization of geospatial data
in diverse renovation tasks. We used these studies to acquire the knowledge for developing this ontology.
The next step is ontology conceptualization and implementation, followed by a verification and
validation step. We verified the ontology through brainstorming with experts in a workshop and
consistency checking using the faCT++ reasoner available in Protégé [30]. In addition, we validated the
ontology against its targeted purpose by conducting a workshop with practitioners and representing the
surrounding geospatial data for a case study in GIS.
22
Figure 2.2. Procedure of developing the ontology.
2.4.1. Literature Review
Some studies implicitly mentioned effective parameters from the surrounding of the building in different
analyses of renovation projects. Nevertheless, we realized that it is challenging for the engineers and
practitioners to identify the most suitable geospatial datasets and workflows in different phases of the
renovation process, for instance in the planning phase. Therefore, we conducted a survey on relevant
studies and collected an explicit list of renovation tasks and the corresponding required geospatial
concepts. We used this survey as a basis for capturing knowledge to develop the ontology.
Among different available approaches for literature review, we applied snowball sampling for selecting
the articles. This method is recommended when it is challenging to access subjects with specific target
characteristics [31]. Therefore, this approach is suitable, as it helps to access those publications which
do not explicitly mention the subject of study. In summary, the procedure starts with a set of relevant
papers. The next round of articles is selected based on the title, abstract, and references provided. This
procedure continues until enough articles are available [32]. Most of the papers are published in journals
that are focused on building in the built environment. This is expected, as the topic is in the conjunction
of building and its surrounding environment. Most of the papers are from the year 2010 onward,
although we did not have any constraint in selecting the papers. We surveyed mainly the articles through
google scholar.
2.4.2. Ontology Development
The research approach for developing the ontology includes ontology specification, knowledge
acquisition, and conceptualization [33].
2.4.2.1. Ontology Specification
Ontology specification is done by answering questions regarding purpose, scope, intended end-users
and intended use of the ontology (Table 2.1).
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Table 2.1. Ontology Specification.
Purpose
This ontology is developed to represent surrounding geospatial and environmental data to
support experts in different stages of building renovation projects.
Scope
This ontology includes real-world physical objects such as building and road, conceptual
objects such as district, and urban processes related to population, environment, and energy.
Intended
end-user
The intended end-users are renovation project practitioners such as site planners, data
collectors, energy experts, performance and comfort analysis experts, and decision-makers
Intended use
The ontology is intended to be used as a common knowledge management framework. This
framework will give a comprehensive view of all the datasets in the surrounding of a building
that can support building renovation.
2.4.2.2. Knowledge Acquisition
The literature review mentioned in Section 2.3 is the basis for knowledge acquisition. After defining the
ontology specification, an initial list of intended uses including specific renovation tasks that can be
supported by this ontology is prepared according to the literature review. For each task, a list of
surrounding datasets that can be required or beneficial is assigned. These specific tasks include site
planning, building energy modeling, acoustic, air quality, thermal and lighting comfort analysis.
Subsequently, brainstorming and experts’ opinions as well as investigating the surrounding environment
of real demonstration sites through aerial imagery, available maps, and visiting renovation sites helped
authors to formalize the knowledge. After the ontology requirements are specified, the next step is to
formalize and conceptualize this specification. For this purpose, a list of entities (objects) along with
some attributes and processes are identified, and some relations are used to connect them.
2.4.2.3. Conceptualization and Implementation
The ontology presented in this research aims at covering all the physical (bona fide) objects in the
surrounding environment in the context of building renovation projects such as building, as well as non-
physical (fiat) objects such as district [29]. The ontology also covers processes that convey information
about the distribution of specific phenomena in a location, for instance, distribution of energy
consumption or potential of renewable energy sources in the surrounding of a building. The ontology is
developed based on the concepts in urban ontology, and in the object view it is inspired by the concepts
in CityGML. Therefore, existing standards and data models are considered when developing this
ontology. To this end, objects and processes associated with some attributes and properties are used as
the starting point.
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2.4.3. Ontology Evaluation
The evaluation of the ontology includes investigating its quality and correctness. These perspectives are
examined through consistency checking of the concepts and axioms and their relations (verification),
and competency checking of the ontology for the purpose it is developed (validation) [19], [34]. Figure
2.3 shows the evaluation effort in summary.
Figure 2.3. Ontology evaluation effort in summary.
2.4.3.1. Ontology Verification
We conducted workshops with participation of five construction engineers researching ontology
development in the AEC domain for verification of the concepts and relations introduced in this
ontology. These experts helped with the verification of the ontology because they have extensive general
background knowledge in ontology development. Furthermore, each of them has developed ontologies
for specific tasks for construction purposes in individual research. In this workshop, we did not focus
on the instantiation of the ontology for a specific project. Instead, we presented a general description of
the concepts and relations in the ontology. The experts discussed based on extensive scrutiny on the
concepts, relations, and the hierarchy between them.
Moreover, consistency checking of the ontology helps for a correct interpretation, which can increase
the quality of the ontology. In this regard, ontology reasoning helps to find the conflicts in the knowledge
content [19]. We implemented the proposed ontology using OWL/RDF language in Protégé [30]. The
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ontology file is available online1, and a visualization of the ontology in provided in Appendix B of the
manuscript.. After considering the comments from experts, we performed an automated consistency
check using the faCT++ reasoner in Protégé Version 5.5.0 [35]. Reasoners perform a consistency check
verifying there is no inconsistency in the concepts and relations.
2.4.3.2. Ontology Validation
The second part of the ontology evaluation is to check the competency of the ontology for the intended
uses for which it is developed. This task is more complicated than verification for two main reasons.
Firstly, the evaluation requires a representation of the ontology within a specific context and for a
particular purpose. Secondly, the task for which the ontology is used should be sufficiently complex
[36]. One approach is implementing a prototype based on the ontology. Using the prototype, it is possible
to establish a demonstration of the model to ask experts and engineers about their opinion. It is also
possible to ask the experts to use the prototype to solve an engineering task within an open-ended
experimental setting, without formal structuring of the process [19].
In this research, the validation of the ontology consists of a workshop conducted to check if the ontology
accomplishes specific tasks, and fulfills the expectations mentioned in the intended end uses in ontology
specification (Table 2.1). A prototype is developed to demonstrate the ontology. The experts had
practical experience with the prototype to retrieve data for a specific case study. In addition, to clarify
how these datasets can be helpful for the experts, we visualized the geospatial data for a case study in
ArcGIS. The main activities for the validation effort include:
Preparation of the prototype: We developed a prototype based on the ontology, that serves as a
repository for retrieving and storing the required geospatial data for building renovation projects. It is
designed based on a service-oriented architecture (SOA) framework for retrieving the required
geospatial data that adheres to the OGC standards of Web Feature Service (WFS) for retrieving vector
data. The retrieved data shall be downloaded in Shapefile or GML format and visualized in a GIS
software. The prototype includes a list of use cases for which the required geospatial data is suggested.
Selection of the participants for the workshop: For selecting the workshop participants, it is crucial
to consider what skills are required to assess the ontology according to the intended end-users of the
ontology [19]. The recipients of the invitation were chosen based on that specific expertise (Table 2.1)
and were asked with direct invitations [37]. Based on the availability, we invited four engineers involved
in building renovation. The experts work in building energy modeling, acoustic, air quality, lighting
comfort analysis, research, and development in building renovation field. These experts are involved in
an EU research project focused on residential building renovation (Horizon 2020 BIM-Speed project
1 http://dx.doi.org/10.14279/depositonce-12787
26
[38]). The project participants are 23 international companies and research groups working on 13
different demonstration cases across Europe. Therefore, the selected experts are directly involved in real
building renovation projects and can reflect on the ontology development from an operational
perspective.
Practical experience with the prototype: In this step, we asked each of the experts to work with the
prototype. We asked them to select the building location on the map, check the list of use cases, choose
the use case that is most relevant to their field of work, check the data list that is suggested for the use
case, and provide their ideas about the listed concepts. We asked the questions in a semi-structured
manner to allow the possibility for brainstorming. Questions included but were not limited to:
• For the available list of use cases, what datasets they would recommend as required or helpful.
• For the mentioned concepts, what other attributes they consider as required or helpful.
• If the hierarchy used to present this ontology is meaningful and logical.
• What other use cases they recommend for utilizing the surrounding data in the building renovation
process.
The questions have been asked in a less structured manner, as suggested by Hartmann and Trappey [19].
It helped to have a more open discussion which subsequently resulted in exploring new ideas to improve
the quality of ontology.
Visualization of geospatial data for a case study in GIS: The benefit of having access to the
surrounding data collected from the prototype can be more evident if the geospatial data are visualized
on maps. For this purpose, we visualized some of these datasets on maps for a specific case study using
ArcGIS software. Envisaging the building in its geospatial context can help better comprehend its
limitations and possibilities. Besides, we asked the experts about their current experiences for collecting
such datasets in renovation projects. Subsequently, we investigated what information can be revealed
from the maps to help in a specific use case in the renovation of a particular case study.
2.5. Results
2.5.1. Knowledge Capture from Literature Review
The studies using geospatial datasets in renovation tasks are summarized and provided in online2. The
renovation tasks are categorized into site planning, building energy modeling, thermal, acoustic,
2 http://dx.doi.org/10.14279/depositonce-12787
27
lighting, and air quality comfort analysis. We selected this list of renovation tasks from an exhaustive
list of use cases for building renovation that is developed in the BIM-Speed EU research project [38].
The participants of the use case development are from construction companies and research groups and
are involved in building renovation projects. From this list, the authors selected those use cases for which
they expect surrounding geospatial and environmental data are required. The following provides a
summary of some of the studies mentioning requirement of geospatial data in each of these use cases.
Site planning: It is believed that the premise for success of the future development in the renovation
projects is site planning [39], [40]. Different studies mention diverse aspects of surrounding datasets in
site analysis and planning such as building data collection [5], primary analysis for building energy
demand [11], logistics and planning for access of workforce and material, safety [40], [41], [42],
regulations caused by historical preservation and interconnection within the heating network and
renewable sources of energy for energy supply management of the building [12]. The surrounding
geospatial datasets can provide information for planning the project in advance and understanding the
limitations and facilities on the construction site.
Building Energy Modeling (BEM): BEM is one of the critical analyses in the building renovation
process. Environmental data, and particularly weather data provided from weather stations, can directly
affect the energy modeling of the buildings [13], [41]. The shading effect of the surrounding obstacles,
such as buildings and trees, is another considerable parameter. For instance, one study which evaluated
the impact of tree shades on the building’s energy demand shows a considerable reduction in energy use
in the summer season [43]. Also, altitude, vegetation, and water bodies that cause evaporative cooling
can affect the local weather condition [44], [45], [46]. In addition, energy consumption in the urban
context is affected by the socio-economic profile of inhabitants, [44], [47], [48]. Consumption schedule
in the building is directly affected by the consumption behavior of the occupants.
Comfort analysis: It is essential to find effective factors in studying the occupants’ comfort from
different aspects, as it is connected to the health issues and well-being of the building occupants [49].
Different studies address the effect of the surrounding built area, roads, walkways, playgrounds, running
water, and pools in the acoustic comfort of the building [44], [47]. Different urban objects can affect the
urban soundscape in the built environment, such as playground zones because of children’s voices, trees,
vegetated areas, and pools, due to running water, footsteps, roads, and walkways on account of traffic,
and human voices [50], [51], [52], [53], [54]. Collecting this information in the early stages of a building
renovation from geospatial data sources provides valuable information for understanding the possible
sound sources in the built environment. These datasets deserve great attention in renovation projects
since correct insulation of facades or replacement of windows can considerably improve indoor acoustic
comfort [53]. Other issues such as air quality, outdoor temperature, wind speed, and wind direction are
also affecting the comfort of the residents and they are all considered as external features. Building’s
height to road’s width ratio is used as an indicator to find the density of the urban area. Dense areas
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(indicated through high ratio values) can weaken the wind circulation that reduces the air dispersion
capability, which leads to less indoor air comfort [53], [54], [55], [56]. Decrease in indoor daylight
availability due to the external obstructions may increase building heating and lighting energy demand
[57].
It is also important to mention that understanding the availabilities of the heat supply at the district level,
and taking advantage of utilizing these sources, along with reducing heat loss through a careful design
of the building envelope, leads to thermal comfort of the occupants [53], [54], [55], [58]. Surrounding
building height and the façade material that cause shading effects impact the interior lighting of the
building and the visual comfort of the occupants [52], [53], [57]. The next section provides a detailed
description of the concepts and relations in the ontology.
2.5.2. An Ontology to Represent Surrounding Environment of a Building
As mentioned before, urban ontology is used as a basis for developing the ontology in this research.
Object and Process are the concepts retrieved from urban domain and expanded in the direction which
is required for renovation task. On the other hand, to account for the urban context, for the Object
concept, a top-down bird’s-eye view is applied to categorize the entities. The bird’s-eye view is the view
which is used to represent geographical features on the maps and aerial images [59]. Some components
in CityGML are also the source of inspiration to define the objects in the urban domain (these
components are highlighted in red in Figure 2.4.
Any object on the surface of the earth hasGeometry to define the representation of the feature. Geometry
is an important aspect of geospatial data as geographic objects are tied to space [60]. We did not add
further details of geometry, with the purpose of keeping the ontology at the conceptual level. The first
view from the top is the District, which includes ZipCode. On the lower scale, a Site i.e., an area with a
specific radius around the building under renovation is presented. A Site includes different Parcels. Each
Parcel is related to LandUseType with the object property hasLandUseType (Figure 2.4). Each Parcel
may include five main categories. These categories are the BuiltArea, Vegetation, Water,
EnergyNetwork, and TrafficNetwork. Each of the categories contains different sub-categories and
different attributes are assigned to them. BuildingBlock and Monument are considered as BuiltArea.
BuildingBlock has an object property hasBuilding which connects it to the Building. Monument has an
object property hasConstraint which connects it to ConstructionRegulationConstraint. It is important
to know the construction regulation of monuments and historical places since it can affect renovation
workflow. Sub-categories of BuiltArea have object property hasAttribute which relates them to some
specific attributes such as Area and Name. Attributes such as Height, FacadeMaterial, RoofMaterial
and NumberOfFloor and Area are assigned to Building.
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Figure 2.4. Object view in the proposed ontology.
The ReferenceBuilding corresponds to the building under renovation. This concept is included in the
context of Site as a sub-category of Building. A Buffer should be created around the ReferenceBuilding
to select the desired surrounding concepts. The Buffer has a Distance to define in which radius the
objects are required to be collected. All the sub-categories under Parcel such as BuiltArea and Vegetation
are connected to Buffer with locatedIn object property.
Vegetation category contains Park, PlayGround, and Tree. Park and PlayGround have Area and Name
attributes, while Tree including SingleTree and StreetLineTree has Height, CrownSize, and TreeSpecies
attributes. Water category includes River, Pond and Lake with Name and Width attributes. The
EnergyNetwork distinguishes between different energy sources such as gas and district heating by
EnergyType attribute. The TrafficNetwork category comprises Airport, Road, Railway, Walkway and
Station with Width, Type and Name attributes, and Parking consisting of ParkingLot and ParkingArea
with Name and Area attributes. Some attributes such as Height and Area are extended with two data
properties namely hasValue and hasUnit for more description. Although, same approach is not used for
multivalued attributes such as FacadeMaterial and EnergyType, as the information related to them is
not in the scope of this study.
Figure 2.5 shows the categorization of different Processes. The main processes which can be helpful in
renovation projects are PopulationRelated, EnergyRelated and EnvironmentRelated processes. The
PopulationRelated process includes those processes which are relevant to the people living in the urban
context such as PopulationAge, PopulationDensity and PopulationEducation. TrafficFlow is also
considered as PopulationRelated process, as it is defined as the interaction of pedestrians and travelers
(i.e., people) in the traffic network. Therefore, it is also connected to some entities in TrafficNetwork
object. EnvironmentRelated process includes particulate matter distribution (PMDistribution),
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CO2Emission, UndergroundTemperature, NoiseLevel, ClimateZone and WeatherData. The appropriate
weather data for building energy modeling requires to include at least six parameters namely dry bulb
temperature, relative humidity, wind speed, wind direction, direct and diffuse solar radiation.
EnergyRelated processes include EnergyConsumption and RenewableEnergySource.
RenewableEnergySource includes WindEnergy, BiomassEnergy, GeothermalEnergy and SolarEnergy.
Photovoltaic and SolarThermal are sub-categories of SolarEnergy. The hasFeed relation is used to
connect different renewable energy sources to ElectricityFeed and HeatFeed, which are two entities
used to define the potential of the renewable energy sources. In addition, GeothermalEnergy is related
to Depth with hasDepth object property, to define in which depth, the HeatFeed is provided. Information
about all the processes mentioned can be provided in District and ZipCode or even BuildingBlock and
Building level.
Figure 2.5. Process view in the proposed ontology.
2.5.3. Ontology Verification
We documented the discussion of the participants of the workshop for verifying the ontology. Based on
that, we recognized a list of deficiencies and recommendations (Table 2.2) and modified the ontology
based on that.
Table 2.2. Recommendations and modifications from verification workshop.
Recommendations
Modification
Include concepts related to the buffer
around the reference building
New concepts were added: Site, ReferenceBuilding, Buffer,
Distance.
New relation was added: locatedIn
Relate concepts to their attributes via
object property rather than data property
In the first version, attributes were assigned as data properties to
entities. In the updated version a new concept has been created
31
named Attribute. An object property namely hasAttribute is
utilized to connect each concept to different attributes.
Finally, with the help of faCT+ reasoner, we discovered no inconsistency in the concepts and their
hierarchy in the ontology (Figure 2.6). There are different purposes for using a reasoner including
consistency checking, classification, and realization of an ontology [61]. In this research, we did not use
the reasoner for classification and instantiation, but only for checking if there are any contradictory
factors in the model.
Figure 2.6. Result of the faCT++ reasoner.
2.5.4. Ontology Validation
Exploring the prototype: In the validation workshop, the experts worked with the prototype. Each
expert selected a use case that was most relevant to their field of work and explored the concepts
suggested for that (Figure 2.7). Then, they provided their suggestions related to missing concepts,
relations, or any other consideration.
Based on the comments from the participants, the validation of the ontology resulted in the inclusion of
some new concepts and relations, that were missing. The experts did not have suggestions for adding
new use cases or specific renovation tasks, and the hierarchy of the concepts and their relations seemed
logical to them. Regarding the necessity of having such an ontology, surprisingly, some experts believed
there is no requirement for such an ontology in building renovation projects, although most of the others
admitted that this ontology is beneficial. They pointed out that the ontology is concise and provides an
overarching framework for required geospatial data while it is not superfluous. The main points
mentioned for each of the use cases are summarized in the Table 2.3. Based on the participants’ feedback,
the authors scrutinized the missing concepts and relations and included them in the ontology.
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Figure 2.7. The prototype implemented based on the ontology.
Table 2.3. Comments from the experts in validation workshop.
Renovation task
Missing concepts
Concerns
Site planning
Orientation of the building,
climate zone
District level information are more beneficial in planning
stage.
Building energy
modeling
Underground temperature
Information about energy sources is useful in connection
with information about culture and population age
(societal data).
Lighting comfort
analysis
Radius of the buffer around
the building, height, façade
material and roof material
of surrounding buildings
A simple extrusion of building can be enough (required),
but information about façade materials in buildings can
improve analysis (beneficial), the radius of the buffer for
data collection is important when studying the
surrounding lighting effect.
Acoustic comfort
analysis
Traffic flow, tree, buildings
Some of the experts think ontology is not required for
acoustic analysis as they believe each software
requirement determines the necessary concepts.
Validating the Ontology for a Case Study: The ontology developed in this research aims at providing
a knowledge framework for geospatial information retrieval to support building renovation. To validate
the ontology, we tested it against a case study. To clarify if the ontology can fulfill the specific goals that
it is intended to accomplish, we focused on employing the ontology for one of the tasks, i.e., information
retrieval for site planning. Using the prototype, we downloaded some of the geospatial datasets
suggested for site planning for a specific case study in Berlin, Germany. Then we created some maps in
ArcGIS software to present them to the experts. We assume it is an appropriate case study for this
33
research, since 1) the building location represents a real case scenario for building renovation, 2) the site
includes urban features such as surrounding buildings, road and railway that can make building
renovation a challenging task, 3) the site is located in an active urban area, where the exterior situation
of the building can affect the building renovation from different perspectives.
Before exploring the maps with the workshop participants, we asked them about the conventional
approaches they use to examine the renovation site before starting the project. One participant mentioned
that collecting information for investigating the construction site is based on the data availability. A
general practice before building renovation is to examine existing two-dimensional drawings, which
may not represent all the data layers of the building context. Another option is using the ‘site plan’ of
the area, which shows the existing and proposed conditions of a given area. They usually include
information about transportation, utilities, vegetation. Based on the available datasets, the expert decides
about the actions required before the renovation. Another participant of the workshop mentioned that
depending on the size of the building under renovation, they may investigate the construction sites and
possibilities for the equipment, accessibility of water and electricity, etc. The first step to collecting such
information is always visiting the site. Although, the expertise and knowledge of the engineer determine
the topics to consider in the site survey. The procedure mentioned by both participants suggests that the
knowledge and expertise of the engineers involved in a renovation project have a key role in selecting
the required contextual data. Therefore, a standard procedure is not available to realize the concerns for
site analysis and planning of the area and to have the knowledge framework to collect the required
datasets. By developing this ontology, we introduced a knowledge framework that helps engineers
investigate the urban context of the building. How to interpret these datasets is beyond the scope of the
ontology. The ontology only provides the knowledge framework for interpretation for the experts. Based
on the information retrieved from these datasets, the experts can interpret and decide on better site
analysis and planning.
As mentioned, to clarify the impact of the suggested geospatial concepts by the ontology, we visualized
some of the surrounding data on maps and presented them to the experts. Some of the maps for this
specific case study are shown in Figure 2.8.
34
Figure 2.8. Maps of the surrounding data for Berlin renovation case study.
The experts believe that the maps show that the building is located in an area covered by historical
objects. Therefore, it is essential to consider possible limitations for the construction. Furthermore, the
building is surrounded by major and minor roads as well as a railway. Therefore, acoustic analysis of
the building is an essential task. Also, information about the roads in the surrounding area can help for
performing activities such as logistic analysis, construction material and work force accessibility.
Moreover, solar thermal potential in the zip code level and solar photovoltaic locations in the building
surrounding provides information about alternative energy sources for the building. The experts believe
that the suggested concepts by the ontology provide the possibility for improving the site analysis. They
mention that the ontology fulfills the purposes, namely information retrieval and providing a knowledge
framework for the specific task of site planning in the building renovation.
2.6. Discussion
The main questions that motivate this study are: 1) based on what knowledge framework surrounding
geospatial and environmental data can support building renovation, 2) If ontology development is
helpful to generate this knowledge framework, 3) How experts and engineers involved in the renovation
process can contribute to development of this knowledge framework.
35
Researchers focusing on BIM and GIS integration promote it as an optimal solution for providing the
data flow between construction and urban domain [62]. However, the expert’s knowledge is crucial in
this stage to determine the required concepts for specific applications of a particular task. When this is
clear, the entities and relations can be represented in an ontology. There are different data models to
represent geospatial data such as CityGML which represents 3D features in cities such as buildings,
road, river, and vegetation. There are also some models within the AEC domain which include
surrounding data. For instance, gbXML is a building data model to facilitate building energy modeling.
In addition to the building information, gbXML includes surrounding data such as buildings and
vegetation, as they can affect the building energy modeling [63]. Even though CityGML in urban domain
and gbXML in energy modeling field are the focus of attention, none of them fit suitably for the building
renovation task. Therefore, this study narrowed down the concepts and relations in the surroundings of
a building to a finite number of concepts and relations required for the specific task of building
renovation as suggested by Wallis [64].
To this end, the main contribution of this research is an ontology that represents a comprehensive view
of surrounding geospatial and environmental data that can support building renovation in different
phases. The ontology comprises surrounding physical and conceptual objects, processes i.e., the
geospatial distribution of specific phenomena, attributes assigned to these concepts, and relations used
to connect these concepts. The development of the ontology started by identifying a list of renovation
tasks and use cases from an available list of use cases from an EU research project and in the light of
existing literature. After developing the ontology, we performed a verification and validation workshop
to analyze the ontology against those use cases. We also suggested a tight involvement of practitioners
and engineers in ontology development, as proposed by Hartmann and Trappey, [19]. Therefore, several
practitioners who are experts in building renovation participated in the workshops, thereby forming, and
expanding the knowledge framework. The experts who participated in the validation workshop have
acknowledged that the proposed ontology can work as a common knowledge framework to help
engineers and decision-makers in the building renovation projects control cost and quality.
One of the limitations of the study is the limited number of experts in the validation workshop. Some
outlook for future research includes involving more experts from more diverse fields within the
renovation workflow to expand the perspective on this topic. Moreover, applying different approaches
in the workshop, such as gaming to have more task-oriented and in-depth discussions are some of the
activities foreseen for future research. New ideas from other experts as well as adding new articles to
the literature review resources may lead to some alteration in the concepts and relations of the ontology.
Therefore, the proposed ontology is an evolving knowledge framework, and it has potential for
expansion in the use cases, concepts, and relations.
Another future research topic is extending existing ontologies and data models from the geospatial
domain such as CityGML. We utilized the concepts in CityGML in this research as a basis for developing
36
the ontology. Nevertheless, implementing a CityGML ADE (Application Domain Extension) is a future
research task. CityGML ADE is a mechanism of CityGML that extends the data model with additional
concepts for particular use cases [65]. Using an acknowledged model such as CityGML makes the BIM
and GIS data integration more straightforward in a potential next stage.
This paper suggests utilizing this ontology for the building renovation application, but one of its
limitations is that it does not demonstrate all aspects of using the ontology and its application in any
practical project. A future task can focus on the instantiation of this ontology for particular use cases. As
mentioned before, reasoner has different applications including consistency checking, classification, and
instantiation, while we only employed it for consistency checking [61]. A future research topic includes
using the reasoner for instantiation. In addition, the scope of the ontology is limited to the surrounding
concepts. Therefore, another limitation of this ontology is the partial information about the building
under renovation and possibilities for including concepts related to sensors connected to the building.
Moreover, there is no extended information about some properties of some concepts such as façade
material, roof material, energy types, and road type. Lastly, different design choices for relating concepts
and their attributes may lead to different acceptable alternatives for the ontology.
The topic of ontology is inaccessible, particularly to the practitioners and engineers for whom it can be
most helpful. For this reason, many practitioners believe ontologies are not beneficial in practice. This
study claims that developing a knowledge framework in the form of ontology provides an opportunity
to bring a more holistic view of the requirement of geospatial data in the renovation workflow in practice
as well as in current and future research. Furthermore, the proposed ontology helps integrating
practitioners' knowledge from the engineering domains to the conceptual field of engineering
informatics.
The ontology has implications in practice for engineers involved in building renovation and software
development. For the former group, as a tool for a common understanding about a particular domain,
while for the latter, as a basis for BIM and GIS integration. It also has an implication for research by
demonstrating that ontology can be used to map knowledge from the geospatial domain for the building
renovation tasks.
2.7. Conclusion
Building renovation is a multi-disciplinary task involving experts from different fields, where most of
them are not aware of the accessibility and benefit of surrounding geospatial and environmental data.
As a result, most of the time, analysis is performed, excluding the impact of context. This necessitates
developing an overarching knowledge framework that includes several renovation stages in a holistic
manner and reflects on the significance of surrounding features in the renovation workflow. This paper
presents this knowledge framework and contributes to the body of knowledge by developing an ontology
37
that serves as a common reference for different expert groups in renovation projects. It helps
practitioners in the construction domain to understand how they can benefit from the data which
describes the surrounding to improve their analysis.
For developing the ontology, knowledge is acquired from previous studies that implicitly mention the
effect of surrounding data in different stages of renovation process. It also includes brainstorming,
obtaining expert knowledge, investigating maps of real demonstration sites, and visiting construction
sites. To evaluate the ontology, we conducted a workshop attended by expert participants involved in
building renovation projects, those supposed to be the end-users of this ontology. Their comments and
concerns have been integrated into the development of the ontology. Nevertheless, ontology
development is an evolving task. Therefore, this ontology has potential for expansion by investigating
the concepts suggested by other experts or redeveloped using available data models from the geospatial
domain such as CityGML.
Acknowledgement
This research project is funded under the European Union’s program H2020-NMBP-EEB-2018, under
Grant Agreement no 820553. Views and opinions expressed are however those of the author(s) only and
do not necessarily reflect those of the European Union. Neither the European Union nor the granting
authority can be held responsible for them.
38
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43
3. Is It Fundamental to Examine the Weather Data
for a Reliable Building Energy Simulation? A
Comparative Study with Different Weather
Datasets
(Status: Submitted) Daneshfar, M., Hartmann, T., Pinzon Amorocho, J. A.; Is it
fundamental to examine the weather data for a reliable building energy simulation? A
comparative study with different weather datasets.
3.1. Abstract
This study investigates the sensitivity of building energy simulation to the selection of different typical-
year weather datasets generated from different periods of record compared to the average annual and
monthly heating demand of buildings in different climate geo-clusters of Europe. Three weather datasets
are employed: 1) a typical-year weather data generated from years before 2000; 2) a typical-year weather
data generated from years between 2006 and 2015; 3) a synthetic weather data produced by modifying
the typical-year weather dataset generated from recent historical weather data with hourly average actual
weather dataset. The simulation results verify that, using different typical-year weather datasets, the
annual variation of energy demand ranges between -4.2% and 13.5%. This is in line with the results of
other studies, representing over/underestimation of building energy demand, when employing different
typical-year weather datasets. The study also suggests a proposed synthetic weather data based on
typical-year weather datasets to increase the representativeness of such data for long-term energy
demand estimation. Also, with the current computer capacity and the availability of historical weather
datasets, the research suggests that utilizing long-term actual weather data provides more cost-effective
and accurate building energy demand estimation, compared to typical-year weather dataset. The research
calls on the standardization organizations to develop new weather data standards that account for the
climate change effect and consider the climate zone of the regions. The study particularly underscores
the significance of weather data selection in renovation studies due to its potential economic impact.
Keywords: actual weather data; building energy simulation; building renovation; typical-year weather
data.
III
44
3.2. Introduction
The existing ambition in Europe for the 2030 climate and energy framework is a 32% improvement in
energy efficiency in the building sector [1]. As a result, in the year 2020, the European Commission
(EC) published strategy frameworks and plans as part of the renovation wave for Europe to boost
renovation in European cities [2]. In renovation projects, an accurate energy simulation is a prerequisite
for calculating the as-built energy demand of the building and designing energy-efficient renovation
scenarios. The building energy simulation is entangled not only with the building’s physical
characteristics, Heating, Ventilation, Air Conditioning (HVAC), and occupants’ behavior but also with
the exterior environmental situation [3], [4], [5]. To ensure accurate results for building energy
simulation in new construction or renovation, it is crucial to use weather data that accurately reflects the
long-term weather patterns of the region throughout the building’s lifespan. While single-year weather
data cannot represent the climatological situation of an area, typical-year weather data synthesized from
the long-term actual historical weather data is a more realistic representation of a location’s climate [6].
Such weather data consists of hourly values of meteorological and solar radiation data for one year. It
denotes the most common weather pattern and excludes extreme conditions [10]. Some studies
compared the energy simulation results using various typical-year weather datasets with the mean energy
consumption of a long period of record [5], [6], [7]. Nevertheless, most of this research has been
conducted on typical-year weather datasets generated from historical data of years before 2000.
Observations show that the warmest years and an increase in the extreme days occurred in the years
after the year 2000 [8], [9]. Therefore, the last 20 years’ weather did not follow the expected typical
conditions. As a result, the methodologies for generating typical-year weather datasets may not be
applicable anymore.
The generation of typical weather datasets began in the 1980s in the US with a more structured approach
compared to Europe [10], [11]. Other national organizations have made multiple efforts to generate
typical-year weather datasets for different areas to support building energy simulation and other
applications [12], [13], [14], [15], [16], [17]. EnergyPlus weather database provides a pool of these
datasets globally and in Europe [18]. However, most of these datasets are generated from historical
datasets before 2000. Recently, European Commission (EC) developed a Typical Meteorological Year
(TMY) database and presented it in the Photovoltaic Geographic Information System (PVGIS) portal
[19]. It produces a set of typical-year weather datasets for different locations from recent actual weather
data, i.e., 2005 to 2020 [20], which can be used for further examination of such datasets in building
energy performance. Based on these existing weather datasets, this research investigates how
fundamental it is to scrutinize the selection of weather data for building energy performance. Is it worth
having the computation burden and complexity of requiring new typical weather datasets, or the time
and cost for developing new approaches to represent the current climate condition better, for having a
more reliable energy simulation result? The climate change effect has emerged as a critical issue in
45
construction and retrofit because of its implication on building energy performance and human health
[21]. Thus, as this is a public health- and comfort-related topic, we also discuss, if it is necessary to
consider new standards and guidelines for developing appropriate weather datasets using up-to-date data
for building energy simulations. To this aim, we investigate the annual and monthly energy demand of
three residential buildings in different climate geo-clusters of Europe using two sets of typical-year
weather data. These include: 1) a typical-year weather dataset generated from historical data of years
before 2000 collected from EnergyPlus sources [18]; 2) a TMY generated from years between 2006 and
2015, collected from the PVGIS portal [19]. We also employ a synthetic weather data that we propose
in this research, generated by replacing the hourly values of dry-bulb temperature in TMY (generated
from 2006-2015) with the hourly average actual dry-bulb temperature of the same years. We compare
the energy simulation results using these weather datasets with the average actual energy consumption
of the building in years 2006-2015. The case studies are renovation demonstration cases of BIM-Speed,
an EU research project [22]. Therefore, the results shed light on performance of buildings under
renovation, with their specific characteristics such as age and material. In the end, we provide
suggestions for the building energy performance community in the context of renovation projects. The
three main contributions of this article are: 1) studying the effect of different typical-year weather
datasets from different periods (including more recent years), methodologies, and sources in European
cities with diverse climate conditions; 2) proposing a synthetic weather dataset generated based on the
typical-year weather data to have a more realistic estimation of the mean energy demand of the buildings.
This research has implications for energy experts at a practical level by proposing strategies for
modifying the existing typical-year weather datasets to make them more representative to real climate
condition. At a higher level, the research has implications for policymakers. We encourage
standardization organizations to consider developing standards for required weather data for building
energy modeling.
The paper is structured as follows: Section 3.3 provides an overview of the research background and the
goal of this research. Section 3.4 presents the methodology applied for data preparation and energy
modeling. Section 3.5 shows the results, followed by Section 3.6, which discusses the findings. Lastly,
Section 3.7 concludes the paper.
3.3. Research Background and Motivation
In this section, firstly, we go through the development of different weather datasets used in building
energy simulations. Afterward, we elaborate more on the studies that compare the result of energy
simulations using various weather datasets. Based on that, we describe the research gap in this domain
and explain the contribution of this study.
46
3.3.1. Weather data in building energy simulation
A conventional approach in building energy simulation assumes that future weather keeps the same
pattern as the past years’ condition. Therefore, one methodology is to employ historical meteorological
data series called multiyear data to consider the lifetime of a building. Nevertheless, applying such big
datasets is time-consuming and costly. Therefore, meteorological organizations introduced typical-year
weather datasets as representative weather formats [23]. Developing typical-year weather data came into
attention initially in 1981 in the US to aid engineers in designing and evaluating energy systems and
comparing their results [24]. A typical-year weather data is a synthetic weather dataset generated by a
statistical methodology to select the closest data to the long-term distribution and the farthest data from
the extremities [25]. It is assembled by comparing the cumulative distribution functions of different
meteorological factors within a long period of record [26]. The selection of the weather parameters and
the weights assigned to them determines the feature of different typical-year weather datasets [26]. Test
Reference Year (TRY) datasets are one of the earliest typical weather datasets, which include
information about temperature, wind, and humidity. The first TRY datasets are generated from the data
of years between 1948 and 1975 for 60 locations across the US. The methodology to create TRY includes
selecting one actual year from an extended period of record. In TRY, months with extremely high or low
mean temperatures are progressively eliminated until one year remains [6]. Therefore, the selected year
represents the typical condition of an area rather than the extreme conditions [23].
The National Renewable Energy Laboratory (NREL) introduced TMY weather datasets in 1981 to
overcome the deficiencies in the TRY, particularly the lack of solar information. The TMY weather
dataset is an hourly weather file for which the value of each month is selected based on a monthly
composite weighting of solar radiation, dry-bulb temperature, dew point, and wind velocity compared
to the long-term distribution of those values. In TMY, typical meteorological months are selected from
more than 10, 20, or 30 years. The criterion for selecting the month is closeness to the long-term weather
condition of the location based on the Finkelstein-Schafer (FS) statistics [6]. FS is a measure that defines
the difference between the distribution of each month and the long-term distribution for the same
calendar month over a period of record. The FS statistics are calculated for various climate factors,
which are then given weights and summed. The month with the smallest cumulative FS is assumed to
be the most representative typical month. In developing the original TMYs, NREL evaluated the
following climate parameters: maximum, average, and minimum dry-bulb and dew point temperatures,
maximum and average wind speed, and total solar radiation. The method is also called the Sandia
method [6]. The first TMY datasets were generated for 234 locations in the US using the data from the
years 1952-1975. TMY datasets were updated once in 1995, referred to as TMY2. The TMY2 datasets
were generated using the weather data from the years 1961 to 1990, the solar information evolved, and
the locations were increased by 248 locations across the US [6]. Subsequently, the TMY3 datasets were
47
developed in 2008 by data from the years 1976-2005. The TMY3 included new enhancements and
developments in the weight selection of the parameters [6], [11], [27].
Parallel to the attempt in NREL, American Society of Heating, Refrigerating, and Air-Conditioning
Engineers (ASHRAE) commissioned the development of a weather dataset to represent a more typical
weather pattern than one year or an assemblage of years in 1985 called as Weather Year for Energy
Calculations (WYEC) [6]. The method to generate WYEC is based on TRY. The process involves
choosing a particular month from the dataset, where the mean dry-bulb temperature is closest to the
average dry-bulb temperature for that month during the recorded period. In the first stage, ASHRAE
generated WYEC for 51 locations in the US and Canada [5]. In the 1990s, they started updating WYEC
by applying better calculation for solar data and extended the datasets to 71 locations and named it
WYEC2 [6].
In Europe, the first set of TRY datasets was generated by Commission of the European Community for
29 locations in Belgium, Denmark, France, Ireland, Italy, the Netherlands, and the UK [6]. They applied
the methodology introduced by Lund and Eidorff [28], which some years after has been revised by
Petrakis [29]. Other reference climate datasets have been created for European countries in a less
harmonized manner, for instance, in Italy, Spain, and Poland for 66, 52, and 61 locations, respectively.
Recently, the European Commission developed a tool for calculating TMY from recent years, from 2005
to 2020, for different cities, called TMY PVGIS [19]. The datasets are produced by choosing the most
typical month out of ten and 15 years of data considering weather parameters such as global horizontal
irradiance, air temperature, and relative humidity. The radiation data included in these weather datasets
are calculated from different satellite images [30]. The methodology to generate the TMY is according
to ISO 15927-4 [31]. This method considers equal weight for air temperature, solar radiation, and
relative humidity when generating the typical months [31].
On the other hand, another thread of research is focused on generating weather datasets for building
energy simulation that include the future scenarios and project future climate condition. Global Climate
Models (GCMs) are generated using numerical experiments based on emission and concentration
scenarios [32]. GCMs project the future climate on the global scale and low temporal resolution and
need downscaling to make them usable for building energy simulation [33]. The two main methods for
downscaling include statistical and dynamical. In statistical downscaling statistical relationships
between local climate variables and global climate data are applied using deterministic or stochastic
approaches [34]. On the other hand, in dynamical downscaling, a Regional Climate Model (RCM) is
used to derive local climate information. RCMs are numerical models that simulate atmospheric and
land surface processes, high resolution topographical data and so on, based on an explicit specified
boundary condition from a GCM [34]. While the former approach reduces the computational time
required for downscaling [32], the latter generates a more consistent dataset which represent a better
spatial and temporal variability of the local climate [34]. After downscaling, the generated years of
48
weather data should be formatted according to the template which is usable in building performance
simulation tools [34].
This paper only focuses on the effect of applying various typical-year weather datasets generated from
historical weather data, and studying the future weather data is not in the scope of this research. The
goal of developing and applying different typical-year weather datasets in building energy simulation is
to assess the long-term average heating and cooling energy use of a building [26]. Many studies
compared the building heating, cooling, and electricity demand using different weather datasets,
including typical and actual weather data. The next session presents a summary of some of these studies.
3.3.2. Building energy simulation using different weather datasets: previous studies
Studying the representativeness of various weather datasets in building energy simulation started in 1995
by Haberl et al. [7], and continued by Crawley [6] and Huang [35], for cities in the US. For instance,
Crawley [6] investigated different weather datasets and building energy use to find the best weather
representative for long-term climate conditions for commercial buildings in the US. His results show
that TMY2 and WYEC2 show simulation results closer to the mean for the period of record. Although
TMY2 and WYEC2 show an annual energy demand variation of -9% to 3.2% and -0.6% to 2.5%,
respectively. He suggested that the TMY2 methodology needs more adjustment to match the long-term
average statistics. In general, his results presented a variation between -11% to 7% in energy use of the
building using different weather datasets. Among other studies, Seo et al. [36] showed a maximum of
5% variation using different weather datasets for ten different locations in the US. Bhandari et al. [3]
compared the impact of weather files collected from various weather sources and web services in one
location in the US. They contrasted the actual weather data with the measured ground truth dataset.
Their results show that annual energy consumption can vary by ±7%, while monthly energy
consumption can change by ±40%. Fikru and Gautier [37] showed that, on average, one unit increase in
heating and cooling degree minutes increases energy use by about 9% and 5%, respectively, for a
conventional dwelling with advanced efficiency features and 5% and 4% for a net-zero solar house with
relatively more advanced features in the US. They also indicated that the sensitivity of building energy
use to weather data highly depends on the season and month and the specific time of the day and night.
Crawley and Lawrie [38] proposed a new regime for climate data representation for case studies in the
US. In their approach, a typical year is generated by assembling extreme months rather than typical
months. They suggested that with the powerful computers we have today, we do not need anymore one
single weather data for building energy calculations.
Other studies that have explored a diverse range of climates have predominantly focused on either China
or Canada. For instance, Cui et al. [39] investigated the energy use and electricity peak load using
typical-year data versus 55-year (1960-2014) actual weather data in 10 cities covering all climate zones
49
of China. Their results demonstrated significant variations in energy use and peak load; therefore, they
suggested adopting multiyear weather data instead of typical-year weather datasets. Furthermore, Siu
and Liao [25] compared energy simulation results using two typical-year weather data, one generated
from the 1990s and one from 2016, and historical weather data from 1998-2014. Their results show that
TMY is not a good representative of climate conditions since past weather data might not be a good
indication of recent and future weather data. Another reason is that the update cycle of TMY generation
is not frequent enough. Therefore, recent years’ weather condition is not considered in such datasets.
They conducted their study in 16 locations in Toronto, Canada, using typical-year weather data
generated from older and recent periods. Their simulation results demonstrate a 6%-30% overestimation
and a 12%-13% underestimation in heating and cooling demand, respectively. Hosseini et al. [40]
investigated the effect of building type and design parameters such as window-to-wall ratio, window
solar heat gain coefficient, and floor construction on the energy demand deviation using typical-year
weather data and average energy demand using 30 years of actual weather data for buildings in Montreal
and Vancouver. Their results show that the maximum deviation of 4.5% in energy demand using
different weather datasets appears for varying design alternatives. Although, the peak load for some
cases reached up to 85% underestimation when using typical-year weather data.
In the global scale, Hong et al. [41] studied the weather impact on peak electricity demand and energy
use with 30-year actual weather data (1980-2009), to calculate the mean energy demand from 30 years,
and TMY3 (generated from 1976-2005) weather data in 17 ASHRAE climate zones across the globe for
large, medium, and small office buildings. Their results show more effect of weather data in peak
electricity demand than the energy use. They suggest applying multiple decades of data to assess the
long-term impact of weather data. They also show that TMY3 is not necessarily a good representative
of average energy use. They demonstrated that the weather effect is higher in cold climates. More general
conclusions indicate that TMY3 simulation results can be significantly different from those of the
‘actual’ weather datasets [5].
Studies in European cities are limited to specific climate zones. Pernigotto et al. [42] investigated
reference-year weather data generated from years 1996-2008 for five locations on north Italy climates
and applied simplified building models for simulation. Their results show significant variations in the
considered locations. Grudzińska and Jakusik [43] compared the energy demand of the building using
TMY generated from years 1971-2000 and collected actual weather data for the years 2001-2012 for
two buildings in Warsaw, Poland. The results show that TMY is applicable for heating demand
calculation but underestimates the cooling demand by 37%. A recent study by Evola et al. [44]
investigated the effect of typical-year weather data on heating demand and peak load of residential and
office buildings in Catania, Italy. They developed different typical-year weather datasets based on recent
weather data, also collected other typical-year weather datasets from other sources and compared their
results in energy simulation. Their results show that IWEC is a good representative for heating demand,
50
while in general typical-year weather data can underestimate peak heating load by 12.5% and peak
cooling load by 18% in residential buildings. They suggest using actual weather data for calculating the
peak load. Moreover, Tsoka et al. [45] highlighted the existence of various stochastic analyses to
generate typical weather data by comparing the results of building energy simulation using three
different methodologies with actual weather data in case studies in Greece. Segarra et al. [46] compared
the energy simulation results at six different temporal resolutions using two different actual weather
data, i.e., on-site weather data and third-party providers in four test sites in Spain, Denmark, and Greece
for 2019. They also performed a sensitivity analysis to find the most important weather parameters and
identified dry-bulb temperature as one of them. Kočí et al. [47] compared the energy simulation results
using TRY and recent actual weather data for eight buildings in Prague. The results showed the warming
trend in the years 2013-2017 and consequently the lower heating demand by 3.95% and higher cooling
demand by 3.96%. Despite the effort done in European cities, the case studies are not selected from
different climate zones. Also, there is still space for more investigation in evaluating various typical-
year weather datasets generated from diverse periods, methodologies, and sources. On the other hand,
most of the studies are focused on comparing existing typical-year weather datasets. Thus, we see a gap
in finding adaptation strategies for existing weather datasets to improve their representativeness. The
following section describes the research gap and elaborates more on the investigation performed in this
paper.
3.3.3. Research gaps and contributions
An overwhelming amount of weather data are collected in different weather stations across the globe to
generate weather datasets for specific applications. Collection of such data and developing weather
datasets that represent the typical meteorological condition of a location is complex and time-
consuming. Also, it can result in a high computational burden for the energy simulation of buildings.
The building energy community is still missing a consensus about the weather data type which can
represent the climate condition of a location realistically, particularly in recent years. The methodology
to generate such weather data is outdated and requires a fresh scrutiny.
On the other hand, when studying the effect of various weather datasets in building energy simulation,
the climate zone of the building is a significant factor. Previous studies in Europe are limited to specific
climate zones [42], [43], [44] and do not examine this effect in a wider region. Other studies which
consider broader areas are either in China, Canada, or the US [6], [25], [36], [39]. In this study, we focus
on three case studies in different climate geo-clusters of Europe. We select three residential buildings in
the European cities of Germany, Spain, and Poland, which are candidate buildings for renovation and
represent typical residential constructions in their countries. For these case studies, we collect a set of
weather datasets from different sources, including typical-year weather datasets generated from weather
data of years before 2000, typical-year weather datasets produced from weather data of years between
51
2006 and 2015, and multiyear actual weather data of years 2006-2015. In addition to the collected
weather datasets, we generate synthetic weather data for each location. We perform energy simulations
using detailed building models and the above-mentioned weather data and analyze the results in terms
of annual and monthly heating demand of the buildings. As a result, we explore how sensitive building
energy performance simulation is to the selection of different weather datasets in Europe. Based on this
exploratory analysis, we aim to answer the following questions:
1. Is it fundamental to emphasize the selection of specific weather datasets for an accurate building
energy simulation in renovation projects?
2. Is typical-year weather dataset a good representative for calculating the average heat demand of a
building?
3. How is it possible to generate a synthetic weather dataset that represents the building's average energy
use more realistically?
3.4. Methodology
Figure 3.1 shows the workflow of this study. First, we select the case studies and collect the required
weather datasets from different sources. In the next step, we create synthetic weather data from the
typical-year weather dataset generated from years 2006-2015 and actual weather datasets of the same
period. We perform energy simulations using these weather datasets. Lastly, we conduct a comparative
analysis of the energy demand with the average energy use of the buildings for the years 2006-2015 and
interpret the findings.
Figure 3.1. The workflow of the study.
3.4.1. Case studies
As shown in Figure 3.2, the case studies consist of three residential buildings selected from different
climate geo-clusters of Europe [48]. All of them are candidates for renovation in building envelopes and
HVAC systems. Therefore, accurate building energy modeling is crucial to evaluate potential renovation
alternatives for them. The selected case studies represent the real case scenarios for renovation studies
52
in an EU Horizon 2020 research project called BIM-Speed [22], and detailed building models of them
are available online [49]. The buildings are also distributed in different climate zones in Europe,
represent the typical structures for dwellings in their countries, and have almost same age.
Figure 3.2. 3D Model of the case studies (left to right: Berlin, Gdynia, and Vitoria).
The case study in Berlin is in the north-western climate geo-cluster of Europe. It is a multi-family
building constructed in 1960 and consists of two separate structures. The building complex has a
basement, one ground floor, and six upper floors. According to the structural inventory, the total building
area is 3574.3 m2. The building has a structure of reinforced concrete with insulation.
The building in Gdynia is a duplex (two-family) residential building with 153.43 m2 area; constructed
in 1961 and located in the north of Poland. The climate geo-cluster of the building is considered central
Europe. The materials used in the exterior wall of the building include brick and plaster. The building is
connected to the natural gas heating system. While the building is partially insulated with expanded
polystyrene, it has low energy performance. The case study in Vitoria is a residential building
constructed in 1958 in the north of Spain, in the southern climate geo-cluster. It is a four-story residential
building with a total area of 838.76 m2 and a garage and a bar on the ground floor. The U-shaped building
has a main structure of reinforced concrete with no insulation. The roof is reinforced concrete, covered
externally by ceramic tiles with no insulation. Due to the lack of insulation, there is poor energy
performance and condensation and humidity problems.
3.4.2. Weather data collection
We collected three sets of weather data as described below:
1. We retrieved the typical-year weather datasets from the weather database of EnergyPlus [18]. These
datasets include typical-year weather data generated from historical weather datasets of years before
2000, including IWEC (International Weather for Energy Calculations) weather data for Berlin,
SWEC (Spanish Weather for Energy Calculations) weather data for Vitoria, and an IMGW
(Meteorologii i Hosomaki Wodnej) weather file developed by the Polish Ministry of Infrastructure
for Gdynia. The IWEC weather data is the result of an ASHRAE research project which provides
typical weather datasets for 227 locations outside the US and Canada, using data from the years
53
1982-1999 [50]. Integrated Surface Hourly (ISH) weather data archives at the National Climatic
Data Center is the source of values for most weather parameters in this dataset. Solar radiation data
is estimated hourly based on earth-sun geometry and cloud amount information [50]. The
methodology acquired to develop this weather dataset is the same as the approach used in TMY
(explained in Section 3.3.1). The SWEC weather dataset, initially developed to be used in Calener, a
building energy labeling tool in Spain, covers 52 Spanish provinces [51]. These weather files are
synthetically generated from mean monthly data collected from the Spanish Meteorological
National Institute for 1961-1990 [52]. The Polish Ministry of infrastructure developed IMGW for
61 locations, based on the data provided by the Institute of Meteorology and Water Management
for the years 1971-2000 [53]. The same FS statistics were adopted, while the selected key
parameters were dry-bulb temperature, solar radiation on a horizontal surface, and relative humidity
[53].
2. We retrieved TMYs generated from years 2006-2015 for each case study from the TMY generator
of the PVGIS portal of the EC, referred to as TMY [19]. This service collects and processes
measured data from the World Meteorological Organization (WMO) weather stations, the solar
radiation data from two different satellite sources, namely, ECMWF ERA-5, with global coverage
at a resolution of about 30 km, only for the time 2010 to 2016, and COSMO-REA, covering Europe
and Northern Africa at the spatial resolution of about 6 km and for the time between 1995 and 2015.
3. We retrieved actual weather data from the MEREEN weather service for the years 2006-2015 [54].
The service collects data from different sources, performs a data quality diagnosis, and returns the
weather data in an EPW file format, which is compatible with EnergyPlus software for energy
simulation. This service retrieves the solar radiation data from SYNOP/METAR data history,
Copernicus CAMS radiation services, and MERRA2 NASA services.
Using the actual weather data, we created a set of synthetic weather datasets by updating the TMY
(retrieved from PVGIS) with the hourly average of actual dry-bulb temperature. The following section
describes the methodology to generate this weather dataset.
3.4.2.1. Generating synthetic weather data (SW)
We replaced the hourly dry bulb temperature values in the TMY dataset, with the hourly average of
corresponding values from actual weather data collected from the MEREEN weather service over the
same 10-year period (2006-2015). An EPW weather file format includes 8760 rows of data (for 365 days
of a year * 24 hours for each day), where each row contains meteorological and solar information for
the station in each hour. The minimum weather parameters included in this file are dry-bulb temperature,
relative humidity, normal solar radiation, diffuse horizontal radiation, and wind speed [6]. SW assembles
8760 rows of data which contain the same values as TMY for all parameters in each row and column
except the dry-bulb temperature. Therefore, the SW is produced using TMY as a basis but deviates from
54
it in two aspects. Firstly, the approach used to determine the dry-bulb temperature values varies between
TMY and SW. Secondly, the sources utilized to obtain dry-bulb temperature values may differ between
the two datasets. Table 3.1 shows all the weather datasets applied in this study, the sources from which
we retrieved them, and their periods.
Table 3.1. A description of weather datasets (EnergyPlus, PVGIS, MEREEN weather service).
Weather data
Weather file
Source
Period
Typical-year weather data generated
from historical data of years before
2000
IWEC for Berlin
EnergyPlus
1982-1999
SWEC for Vitoria
EnergyPlus
1961-1990
IMGW for Gdynia
EnergyPlus
1971 - 2000
Typical-year weather data generated
from historical data of years after
2000
TMY
PVGIS
2006-2015
SW: Proposed syntenic weather data
Updated TMY with hourly
average actual weather data
for dry-bulb temperature
PVGIS/
MEREEN
2006-2015
3.4.3. Comparison of energy simulation results using different weather datasets
After preparing the weather datasets, we perform energy simulations for the three case studies and the
weather datasets, i.e., IWEC, SWEC, IMGW, the three TMYs, the three SWs, and the detailed building
models using EnergyPlus, a prominent energy analysis tool for buildings [55]. The results of simulations
provide information about the annual and monthly energy consumption of the buildings. In addition, we
perform energy simulations using the actual weather datasets for the years 2006-2015 to calculate the
average building energy use for ten years. We compare the simulation results using different weather
datasets with the mean energy use of the buildings. Then we interpret the results.
3.5. Result
For each case study, we performed 13 simulations, one with IWEC, SWEC, or IMGW, one with the
TMY, one with the SW, and 10 with actual weather data, thereby 39 simulations altogether. The results
of the analyses are presented in the following.
Figure 3.3 shows the annual energy consumption of the three case studies using different weather
datasets. The darker bar in each figure represents the annual average energy consumption for ten years
(2006-2015), using actual weather data. Figure 3.3 also includes energy consumption using single-year
weather data from 2006-2015. For the Berlin case study, annual energy consumption using IWEC and
55
TMY weather data overestimated the energy demand of the building compared to the annual average
energy consumption for the years 2006-2015. Despite the two other weather datasets, SW demonstrates
a more realistic estimation. For the building in Gdynia, the IMGW, TMY, and SW do not represent
dramatic variations compared to the annual average energy consumption of the building. For the case
study in Vitoria, SWEC and TMY overestimate the energy consumption of the building in comparison
to the building’s annual average energy consumption for the years 2006 to 2015. Also, using SW shows
an overestimation, however with a much lower value.
Figure 3.3. Annual heat demand using different weather datasets.
Based on Figure 3.3, the year-to-year annual heating demand variation can be seen less in Vitoria, which
is in the southern geo-cluster of Europe. For the other two case studies, the year-to-year variation is
relatively higher. In the Berlin case study, which is in the north-western geo-cluster of Europe, the annual
average heating demand is relatively less than the estimated energy demand by the two typical weather
datasets. According to the European Environment Agency [56], the impact of climate change in this
region will result in a decreased demand for heating in the future, which is not reflected in the results
when using typical-year weather datasets.
56
Figure 3.4 shows the monthly energy consumption using different weather datasets versus the monthly
average energy consumption of years 2006-2015 for each case study. The focus is on winter months, as
the simulations only calculate the heating demand of the buildings.
Figure 3.4. Monthly heat demand using different weather datasets.
Some observations from Figure 3.4 are as follows:
1. In general, the typical-year weather data mostly overestimates the energy consumption compared to
the monthly average demand.
2. In all the cases, using SW presents low variation in the energy consumption compared to the monthly
average energy demand.
3. Using typical-year weather data disregarding its period in most cases does not represent the average
energy demand of the buildings well.
For more detail, we calculated the percentage change ratio in energy consumption using the typical
weather datasets and the SW and compared their results with the annual and monthly average energy
consumption. As shown in Table 3.2, the range of annual heating demand across the three locations in
Europe varies from almost -4.2% to 13.5% for the typical-year weather datasets.
For the Berlin case study, SW shows less annual variation compared to the two typical weather datasets
in comparison to the average monthly energy consumption. The variation in annual energy consumption
calculated using TMY and IWEC datasets is almost the same. The monthly variation of energy demand
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using TMY ranges between -15.2% and 45.6% in March and December respectively, while using IWEC,
it ranges between -4.2% and 23.9% in January and November, respectively. Using SW exhibits a
variation in the range of -1.3% and 2.8% in March and December, respectively.
The annual heating demand of the building in Gdynia is underestimated by approximately 4.2% when
using IMGW data, while TMY and SW overestimate it by 2.5%, and 3.4%, respectively. Although TMY
shows less annual energy demand variation than the other datasets, it exhibits a maximum monthly
variation of around 27% in March, which is significantly higher than the other datasets. Using TMY
also depicts an underestimation of energy demand in January with 11.8%. Using IMGW, the monthly
variation ranges between -21.3% and 7.2% for January and November, respectively, while applying SW
results in a variation between -4.1% and 8% in November and December, respectively. When analyzing
the building in Vitoria, it was found that TMY and SWEC result in an annual overestimation of
approximately 10.5% and 11%, respectively, while SW leads to a 5.9% overestimation. While TMY
shows an overestimation between -15.6% and 28.2% in February and November, respectively, SWEC
results in a variation between -1.3% and 19.4% in February and November, respectively.
Table 3.2. Percentage change ratio for annual and monthly energy consumption using different weather
datasets compared to average energy consumption.
Berlin case study
Gdynia case study
Vitoria case study
TMY
IWEC
SW
TMY
IMGW
SW
TMY
SWEC
SW
January
-0.3%
-4.2%
-0.9%
-11.8%
-21.3%
-1.7%
17.1%
7.3%
3.1%
February
18.2%
8.9%
2.5%
11.8%
-8%
5.7%
-15.6%
-1.3%
3.5%
March
-15.2%
6%
-1.3%
27%
2.3%
7.1%
16.4%
19.3%
15%
November
-2.4%
23.9%
1.5%
-3%
7.2%
-4.1%
28.2%
19.4%
1.3%
December
45.6%
5.5%
2.8%
-0.8%
-6.2%
8%
15.5%
8.6%
4.2%
Annual
13.5%
12.3%
1.7%
2.5%
-4.2%
3.4%
10.5%
11%
5.9%
3.6. Discussion
The main questions that motivated this study are: 1) Is it fundamental to meticulously select the weather
datasets for an accurate building energy simulation in renovation projects? 2) Does the typical-year
weather data represent the mean energy use of a building? 3) Can a synthetic weather dataset, generated
based on the typical-year weather data represent the building’s average energy use more realistically?
To address the research questions, we conducted multiple building energy simulations using two distinct
typical-year weather datasets. One set was generated with historical weather data of years before 2000
(referred to as IWEC, SWEC, and IMGW), while the other dataset was generated from historical data
58
of years between 2006 and 2015 (referred to as TMY). Subsequently, we compared the outcomes of the
annual and monthly energy demand to the average energy usage of the building with actual weather data
of years 2006-2015. As an alternative solution to represent long-term weather conditions, we generated
a synthetic weather dataset (SW) by replacing the hourly values of dry-bulb temperature in TMY with
the average of hourly dry-bulb temperature of years 2006-2015.
The findings of this research demonstrate that for the selected case studies, the annual energy demand
of the buildings using the typical-year weather datasets generated from years before 2000 demonstrates
a variation between -4.2% and 12.3%, while employing typical-year weather data generated from years
between 2006 and 2015 shows a variation between 2.5% and 13.5%. Unlike the other weather datasets
examined in our study, the synthetic weather data (SW) exhibited less variability in energy use compared
to the average energy demand during the recorded period. Across all case studies, SW indicated a
maximum overestimation of less than 6%.
A survey on other studies in the European context demonstrates a variation between -2.6% to 6.3% for
heating demand of buildings in Warsaw, Poland [43]. They also mention that TMY is a good
representative for mean heat demand calculation. The finding of the current research is consistent with
their statement, as our research on the Polish case study shows a lower variation of energy demand with
typical weather datasets, as compared to the mean energy demand. In another study, Hong et al.[41]
express that climate change has a greater impact on colder climates. Our findings demonstrate that
buildings in the southern climate are also susceptible to the selection of weather data.
In general, studying the impact of weather data on building energy modeling in Europe is very limited,
and requires more investigation. This research contributed to the body of knowledge by utilizing real
case studies and incorporating detailed building models, and a broader range of typical-year weather
datasets within the European context, spanning across multiple climate zones. The results underscore
the significance of meticulously selecting weather datasets for accurate building energy simulations,
since typical-year weather data may not reflect the real energy usage of a building. Our findings do not
support this notion that generating typical-year weather data from more recent datasets is helpful to have
more accurate weather datasets [5], [41]. Currently, ISO 15927-4 [31] is predominantly used to generate
typical-year weather datasets. We propose scrutinizing and reflecting more on the methodology applied
to generate typical-year weather datasets. We suggest that it is essential to consider the intended
application of the weather data (e.g., building energy modeling, solar or wind potential calculation) and
the study location when selecting weather parameters and assigning weight [39].
While our study provides insights into the sensitivity of building energy models to the selection of
various weather datasets, there are several limitations that should be considered. Firstly, our analysis
focused solely on the heating energy use of buildings and did not include cooling energy demand and
peak loads. In addition, our study only examined weather data for specific climate geo-clusters in Europe
59
and may not be generalizable to other regions. However, our case studies are candidates for renovation
and serve as typical examples of residential construction in their respective countries. Furthermore, the
TMY (i.e., typical-year weather data, generated from the years 2006-2015) utilized in this study was
generated from ten years of historical weather data. Therefore, we propose exploring TMYs generated
from more extended periods, including recent years, to cover the lifespan of a building. Also, our
synthetic weather data approach may not accurately capture extreme weather events that could impact
building energy use. Lastly, in this research, we only focused on typical year weather datasets generated
from historical weather data. Future weather datasets generated using various methods are not in the
scope of this research. However, comparing the results of simulation when using historical and future
weather data is an interesting topic which is envisaged for future.
Based on our research, professionals in the building energy industry should expect significant variations
in building energy consumption when estimating average heating demand using different typical-year
weather datasets, including those generated from recent historical weather data. If the typical-year
weather data does not provide an accurate estimation of the mean energy use of the building, we
recommend adjusting the typical-year weather data using average of recent actual dry-bulb temperature.
Obtaining dry-bulb temperature data for a particular location is simpler than acquiring the
comprehensive parameters necessary to generate multi-year weather data that is suitable for building
energy simulations. Furthermore, given the current computer capacity and the availability of historical
weather datasets from various services, utilizing long-term actual weather data is a more cost-effective
and accurate approach.
Irrespective of the weather dataset that is supposed to mirror the real weather conditions of a location,
these disparities indicate that the choice of weather data has a considerable impact on the outcome of
building energy models. However, energy experts often overlook this crucial factor. Thus, it is
imperative to stress the significance of selecting appropriate weather datasets for precise building energy
simulations. We recommend inspecting the available weather data thoroughly before proceeding with
simulations by scrutinizing their period, source, and development methodology. Finally, inaccurate
estimation of a building’s cooling and heating demand may hinder the building’s compliance with
requirements and cause an overestimation of construction costs. This issue is especially fundamental in
renovation projects since financial resources can be allocated to other essential aspects of the building.
Although our study focused on this issue in the context of renovation projects, it is also relevant for new
building construction. We emphasize that this issue is pertinent to public health and must be given
immediate attention by policymakers and decision-makers in the construction and refurbishment sector.
60
3.7. Conclusion
An accurate building energy simulation is necessary for calculating the heating demand of the building
in energy-efficient building design and retrofit [3]. The results of this study provide insights about the
impact of weather data selection on building energy models, specifically with regards to energy use in
renovation projects. We estimated the annual and monthly heating energy use by applying typical-year
weather datasets generated from different periods and sources. The annual heating demand of the
building shows a variation between -4.2% and 13.5%, for the three case studies in different geo-clusters
of Europe. We additionally created a synthetic weather data (SW) by adjusting the existing typical-year
weather datasets. Our findings indicate that SW overestimated the average actual energy demand by less
than 6%, which is a more accurate estimation compared to the typical-year weather datasets. Our study
suggests that with the increasing capacity of computers, the most suitable approach to represent the
average building energy use is to calculate multi-year energy demand. Alternatively, when using
existing typical-year weather datasets, energy experts can adjust them using more recently available
actual weather data. In this study, we have provided an example of such a modification. Moreover, we
believe that policymakers and standardization organizations should play a significant role in developing
new weather standards and guidelines to ensure that the external environment of a building is accurately
reflected in the energy modeling process.
Acknowledgement
This research project is funded under the European Union’s program H2020-NMBP-EEB-2018, under
Grant Agreement no 820553. Views and opinions expressed are however those of the author(s) only and
do not necessarily reflect those of the European Union. Neither the European Union nor the granting
authority can be held responsible for them.
61
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65
4. The Inter-Building Effect (IBE) in Evaluating
Building Performance of Renovation Projects: The
Case of European Cities
(Status: Submitted) Daneshfar, M., Hartmann, T.; The inter-building effect (IBE) in
evaluating building performance of renovation projects: the case of European cities.
4.1. Abstract
Typically, when evaluating building performance in renovation projects, the focus is on the building’s
characteristics without considering the impact of the surrounding urban context. However, numerous
studies have shown that the Inter-Building Effect (IBE) can influence building performance
substantially. This study aims to investigate how sensitive building performance is to the shading effect
of neighboring buildings in the context of renovation projects. The study builds upon previous research
and expands simplified urban block configurations and include randomly selected heights and distances
for surrounding buildings in a nine-block building network. The heat demand and illuminance level in
specific zones are evaluated in residential buildings in two European cities, namely Gdynia and Berlin.
The results demonstrate significant effect of such parameters on simulation outcomes, emphasizing the
necessity of considering the effect of current and future urban developments in renovation projects. The
simulations reveal a maximum annual increase of 8.7% in energy demand and a maximum decrease in
daylight by 64.8%. Additionally, the study found that the IBE is more pronounced in mid-rise buildings
in terms of thermal demand and more significant in low-rise buildings and the south-facing zone of a
building in terms of daylight capture. These findings can propose valuable insights for energy experts
and designers to make informed, and realistic decisions when selecting renovation scenarios and
techniques. Based on these findings, the study also proposes a workflow to integrate the IBE into the
process of renovation projects.
Keywords: building performance; building renovation; Inter-Building Effect (IBE); shading effect.
IV
66
4.2. Introduction
Buildings are responsible for 40% of energy consumption and 55% of electricity use in Europe [1].
Renovation offers an opportunity for building redesign, resulting in more energy efficiency [2]. Accurate
estimation of as-built building performance and renovation scenarios is a prerequisite in renovation
projects. Building Performance Simulation (BPS) is an analytical process to help designers evaluate the
energy performance of the building. BPS of a target building is significantly affected by surrounding
objects of the building through shading and microclimate effects [3]. Therefore, incorporating urban
complexities can enhance BPS results [4]. Surrounding objects such as buildings and trees can cause a
shading effect on the facade of a target building, resulting in a change in the energy demand and visual
performance of the building due to varying solar gain. The combined effect of compactness in buildings
on thermal and visual performance is called the inter-building effect (IBE) [5], [6].
Previous studies incorporated the urban morphology, characterized by building density, height,
direction, and typology [7], [8], [9], [10], [11], [12], [13], within various climate zones [5], [10], [14]
into the energy performance simulation of the buildings. This effect has been studied extensively
concerning the heating, cooling, and lighting demand, and inhabitant’s thermal and visual comfort [6],
[15]. The results generally show an underestimation of the heating and an overestimation of the cooling
energy consumption of the buildings, when surrounding context is excluded. Furthermore, different
building layouts create various lighting consumption habits for the inhabitants and can influence the
indoor visual comfort of the occupants [15].
Regarding urban typologies and layouts, prior studies only focused on the typical urban typologies,
namely slab, pavilion, and courtyard, known as Martin & March’s typology in its simple form (i.e.,
surrounding buildings with same height and distance from the target building) [16] or extended it to
limited complexities [17]. Additionally, some studies investigated the real urban context. The goal of
these studies was to either estimate the energy demand in the urban level or calculate the energy demand
of a target building in the center of a nine-block network. This research is built upon previous studies
concerning studying the energy performance of a stand-alone building affected by shading effect of
surrounding buildings in various typologies. To this end, the paper addresses the effect of more random
hypothetical typologies generated from various combinations of surrounding buildings on energy
demand and visual characteristics of two buildings in two climate geo-clusters of Europe. By applying
a methodology focusing on the building simulation, we envisage the sensitivity of building performance
to various urban layouts and provide insights for energy experts to take measures for adaptation by
adjusting renovation scenarios. We intend to provide knowledge for decision-makers about the relative
importance of urban context in individual building renovation in the preliminary stages of a renovation
project and point out potential renovation configurations that can improve energy efficiency. The result
67
of this research has implications for planners and decision-makers in building design and retrofit, as it
enables them to evaluate performance of building models more realistically.
This paper is structured as follows. Section 4.3 provides a literature review on the topic and describes
in detail the contribution of this article. Section 4.4 describes the methodology applied for the analysis.
Section 4.5 presents the results, while Section 4.6 discusses the findings. Lastly, we conclude the paper
in Section 4.7.
4.3. Literature Review
The shading effect of surrounding buildings on building energy performance has been studied
extensively [6], [18]. There is a consensus about the significance of urban form impact on energy use.
However, the magnitude of its influence is debatable [18]. Urban form, in this context, refers to the
physical form which includes the geometric characteristics of buildings and their layout as well as
landscape elements such as land use, land cover and vegetation. Therefore, various existing definitions
for urban form adds another complexity to this topic. Different measures for defining the urban form
include geometry, density, typology, land use, and land cover (LULC) (Figure 4.1) [18]. For a more
detailed description of each of these measures, refer to [18]. Regarding typologies, which refer to
representation of a group of buildings, many studies applied hypothetical urban layouts following Martin
& March’s prototype [16], including slab, pavilion, and courtyard (Figure 4.1). Other studies considered
real urban structures representing the actual development of an area [19], [20].
Within the existing research, there is a consensus that the multi-family housing is more energy efficient
than the single-family housing due to more compact form and shared walls of the former which results
in reduced heat loss [18]. In general, studies show that in Paris and Shanghai, contemporary
neighborhoods are the most energy efficient form in terms of heating, followed by very old ones, while
neighborhoods that are not too new nor very old are the worst [18]. Other studies are also focused on
studying best urban planning approaches for generating urban forms which result in lower urban energy
use [10], [21], [22], [23], [24], [25], [ 26], [ 27].
Since 2012, a thread of research has been introduced which is focused on investigating the Inter-Building
Effect (IBE) on building energy demand and performance [5]. The focus on these studies is to examine
the extent of IBE of various typologies on the building energy consumption in terms of heating, cooling,
and electricity demand of lighting [26]. Among selected studies, IBE causes an increase in energy
consumption including heating, cooling, and electricity demand in various locations across the US,
China, and tropical areas such as Iran. Selected list of these studies is provided in Table 4.1.
68
Figure 4.1. Urban form measures - authors’ representation adopted from [16], [18].
Table 4.1. Selected studies of IBE in building energy performance.
Reference
Building Type / Area of Study
Findings of the Study
[28]
Residential buildings with typical
urban layout in Hong Kong.
- Cooling demand can be reduced by 18% due to shading
effect of surrounding buildings.
- The lower the floor height, the greater the impact of the
shadow of surrounding buildings.
- Buildings facing southwest has a greater reduction in
cooling demand
[5]
Typical block of Albany, New
York, simulated with weather
condition of Minneapolis and
Miami.
- In Minneapolis, increase in energy consumption
between 22% and 33% in different months.
- In Miami, increase in energy consumption between 20%
and 43%.
[9]
Hypothetical eight-building block of
Miami, Washington, Minneapolis.
AND real building network of
Perugia, Italy, considering the
effect of shadow and reflection.
- The result of energy consumption changes month-by-
month and is dependent upon location and climate
condition.
- The maximum cooling demand occurs in June and May
for more than 40%.
- Mutual occlusion increases the lighting energy
consumption, particularly in high-density urban
environments.
69
[29]
High-rise building in China.
- The shading effect has low impact on energy
consumption for cold winter and mild summer for high-
rise building.
- It reduces energy consumption by 7% in hot summer and
increase energy consumption by 7.58% in severe cold
zones.
[10]
Residential building in various
climates of China.
- The shading effect (of nine-building block with fixed
height and distance from each other in the block)
increases the heating demand by 20% and decrease the
cooling demand by 10% to 20% depending on the
location and climate zone.
[14]
Residential and office building / six
urban density canyon with aspect
ratio of 0.5 to 0.3 in Copenhagen,
Denmark.
- The shading effect of surrounding buildings on total
energy consumption increases up to 30% in office
buildings and 19% in residential buildings.
- The increase of urban density has less effect on
residential buildings than office buildings.
- The energy consumption for lighting changes for various
H/D ratio from double to six times. The lower the floor,
the more the lighting energy consumption is affected by
the IBE.
- Direction of the building is an important factor in
lighting energy consumption of the building. With
increase of building density, lighting energy use of north-
facing building may be lower than that of south-facing
building.
[22]
1600 urban configurations
considering various density, layout
and building form of surrounding
buildings in hot-arid climate of Iran.
- Shading effect causes an improvement in cooling
demand by 10%.
- The study generates best urban configurations with
highest ventilation possibilities and lowest cooling
demand.
[23]
Single building with HR envelope
(Scenario A), IBE with HR envelope
(Scenario B), IBE with LR envelope
(Scenario C) in five cities of Japan.
[High Reflectance: HR, Low
Reflectance; LR]
- Scenarios B and C has lower energy uses. Degree of
reduction is more obvious in scenario C. High reflectivity
of building envelope impacts the IBE (shading effect) to
a great deal.
[24]
Office building with different
scenarios including conventional,
cool, and thermochromic coatings
applied on the roof or on the building
facades, also under several climate
- Thermochromic paints can decrease the cooling demand
by 1.7%. Therefore, it is beneficial in reducing the IBE
effect.
70
change scenarios in Toronto,
Canada.
[25]
Typical eight-story office building in
different surrounding building
layouts in Tel Aviv.
- Used sky solid angle (SSA) and average daylight factor
(DFavg) as evaluation indicator. Results show that SSA
value of at least 1.4 sr is required to get DFavg of about 3%
and 2.2 sr to get DFavg of 5% which is hard to achieve in
lower floors.
[sr: square radian]
[26]
Office building on the first floor of a
two-story building inside a
university campus in Perugia, Italy,
which contains a group of seven
buildings located at different levels.
- The energy performance prediction deviations resulting
from lighting IBE are greater than those from heating or
cooling in a case study building. The analysis has been
replicated for four different building orientations and non-
negligible primary energy requirement modeling errors
are observed irrespective of orientation.
[27]
Typical commercial building in
Hong Kong.
- With severely obstructed surrounding buildings, the
power reduction in each peripheral area is 25-28 kWh/m2,
when the angle of the obstacle changes between 25o and
30o, the lighting energy savings is reduced from 40
kWh/m2 to 28 kWh/m2.
[12]
Investigating the effects of the
orientation, distance, and size of the
neighboring object on heating and
cooling energy requirement of a
representative two-story detached
house in Halifax, Toronto, Calgary,
Vancouver in Canada.
- Annual heating and cooling energy requirement of a
house may be affected by as much as 10%, and 90%,
respectively, by the existence as well as the orientation,
size, and distance of a neighboring obstructions.
4.3.1. Research gaps and contributions
Table 4.1 reveals that most of the research conducted to investigate IBE in building performance are
focused on cities in China, the US, and arid areas. In Europe, only two studies exist in Italy and Denmark.
Therefore, there is a lack of investigation in European cities and climate. On the other hand, existing
studies investigate how the result of heating and cooling demand and daylight of a target building in the
center of a nine-block network gets affected by shading effect of typical urban typologies, namely slab,
pavilion, and courtyard, known as Martin & March’s typology, or the real urban context. Therefore,
there is a lack in scrutinizing more complex hypothetical conditions to consider possible future urban
developments.
In this vein, we investigate two typical buildings located in different climate geo-clusters of Europe. We
perform energy simulations for different buildings and urban built environment models: stand-alone
building, a simplified Martin & March’s typology with constant height and distance of surrounding
buildings, and a hypothetical typology with varying randomly selected heights and distances of
surrounding buildings. we answer the following questions:
71
1. How sensitive is the annual and monthly heating demand and daylight performance of selected target
buildings under renovation located in various urban typologies compared to stand-alone building?
2. How different is the results compared to previous studies?
3. How can this information support renovation projects of these case studies?
Section 4.4 describes in detail the methodology we applied to develop this research.
4.4. Method and Material
Figure 4.2 illustrates the methodology applied in this study. The first step is to identify the buildings for
investigation. The building in Berlin is a typical multi-family residential building, while the building in
Gdynia is a duplex dwelling. Characteristics of typical buildings in different countries is described in
[30]. The two buildings are appropriate for this research as they represent different urban contexts, which
help reflecting on IBE from various perspectives. They are located in different climate zones of Europe,
and represent buildings with various characteristics, including different heights (the building in Gdynia
is low-rise while the building in Berlin is a mid-rise dwelling).
For each case study, the inter-building effect (IBE) of seven various urban contexts is investigated as
compared to the stand-alone building. The seven identified urban contexts include a simplified Martin
and March’s typology, and six complex urban typologies generated from randomly selected heights and
distances of the surrounding buildings. The IBE is calculated for two metrices namely heat demand and
daylight. The integrated building and urban context have been generated using R, and EnergyPlus [31],
a prominent energy analysis tool for buildings, has been used to evaluate the building’s year-round
thermal demand and indoor natural lighting.
The research method starts with technical assessment of the building including the building geometry
and material followed by the weather data selection and urban condition assessment and generation. The
combined building and urban context model and weather data constitute the input data for EnergyPlus
simulation software. We perform energy simulations aiming at results on annual and monthly basis.
Building’s energy demand is aggregated by space heating and cooling, lighting, and ventilation [4]. The
scope of this study does not cover any cooling system inside the buildings; therefore, the modeling
results are limited to the energy demand required for building’s heating. In addition, we estimate the
daylight by retrieving the solar gain through the windows of the building. In simulations, a Typical
Meteorological Year (TMY) weather data is applied which is retrieved from EnergyPlus weather
sources.
72
Figure 4.2. Methodology of the research.
4.4.1. Building models
The building in Berlin is a multi-family apartment block constructed in the 1960s, with poor efficiency
and gas central heating system. It consists of two separate structures. The building complex has a
basement, one ground floor, and six upper floors. According to the structural inventory, the total building
area is 3574.3 m2. The dwelling has a structure of reinforced concrete with insulation. The building in
Gdynia is also a representative of a single-family duplex residential house in Poland built in 1961 and
is located in the north of Poland. The materials used in the exterior wall of the building include brick
and plaster, partially insulated with expanded polystyrene. It is connected to the natural gas heating
system, has low energy performance, and has a total area of 153.43 m2.
For both buildings, a detailed building model is employed for energy modeling. The information about
materials used in the buildings, insulation of exterior walls and roofs for both case studies as well as all
building models used in this study are provided online [32]. The case study in Gdynia has six thermal
zones, namely the Room, Hall, Utility room, Bathroom, Kitchen and Boiler room, and different
thermostat setpoints for heating is applied for each zone (Room: 20c, Hall: 17c, Bathroom: 24c).
Each apartment of the building, in Berlin has six thermal zones, namely the Living room, Bedroom,
Kitchen, Hall, Corridor and Bathroom, and the thermostat setpoint for heating in all zones is assigned
to 20c.
To investigate the shading effect of surrounding buildings on the daylight estimation, we calculate the
illuminance level of selected zones of the case studies in the last floor of the buildings (Figure 4.3).
73
Figure 4.3. Placement of daylight control (left to right: Gdynia, Berlin).
Calculating the illuminance is dependent on several factors, such as sky condition, sun position, location,
glass transmittance of the windows, window shading devices, reflectance of the interior objects and
surfaces [33]. For the case studies, we do not consider any shading system for the buildings and any
blinds for the windows. The glass transmittance of the windows and window-to-wall ratio of each
construction is provided in Tables 4.2 and 4.3. The daylight control method in EnergyPlus, with no
additional user input calculate the daylight that passes through the windows into the zone. The
methodology used to calculate the illuminance level in the zones is Split-Flux [34]. We applied daylight
control in the selected zones of the building that encounter sunlight inside the building due to existing
windows. For the Gdynia case study, two daylight controls are employed in the kitchen and room space
of the second floor, while for the Berlin case study, we embedded two daylight controls in the living
room and bedroom on the sixth floor (Figure 4.3). Figure 4.4 shows the orientation of each space in the
building that include the lighting control.
Figure 4.4. Daylight evaluation spaces in the case studies left to right Gdynia and Berlin case studies.
Table 4.2. Window glass characteristic of the buildings.
Glass U-Factor [W/m2-K]
Glass SHGC
Glass Visible Transmittance
Gdynia case study
3.304
0.762
0.698
Berlin case study
4.443
0.704
0.605
74
Table 4.3. Window-wall characteristic of buildings (North: (315 to 45 deg), East: (45 to 135 deg), South:
(135 to 225 deg), West: (225 to 315 deg)).
Window Opening Area [m2]
Above Ground Window-Wall Ratio [%]
Gdynia case
Total
25.41
15.02
North
10.32
15.50
East
3.15
6.20
South
11.95
28.31
West
0.00
0.00
Berlin case
Total
457.83
18.67
North
275.63
29.48
East
21.95
7.03
South
138.41
15.50
West
21.85
7.01
4.4.2. Calculating the Inter-Building Effect (IBE)
To quantify the IBE for the target building situated within the building block network, we apply the
approach proposed by Pisello et al. [5]. Based on this approach, the simulation results of a building
model including the urban context is compared to the simulation results of a stand-alone building
(Formula 1).
𝐼𝐵𝐸 =
∑𝐻𝑃𝐼𝑛,𝑖−𝐻𝑃𝐼𝑠,𝑖
𝐻𝑃𝐼𝑠,𝑖
𝑤
𝑖=1
𝑤∗100 (1)
Where:
𝑤: number of months during which IBE is calculated.
𝐻𝑃𝐼 𝑛,𝑖: heat demand/illuminance level of the control building within the building network for the month
i.
𝐻𝑃𝐼𝑠,𝑖: heat demand/illuminance level of the stand-alone building for the month i.
For each building, seven various urban block and their IBE compared to standalone building are
investigated. The seven urban block conditions include:
75
a) Simplified Martin and March’s typology: looking at the real urban context of the buildings (Figure
4.5), the dominant geometrical typology in the urban context of the case study in Gdynia is pavilion,
while for the case study in Berlin is mixed pavilion and slab. Therefore, we assume the two selected
buildings in Gdynia and Berlin are in a Pavilion and Slab urban typologies, respectively. For
simplification, the length of the surrounding buildings is considered the same as the reference
building, and the distance and height of the surrounding buildings are considered constant (Figure
4.6). Therefore, for the building in Gdynia, which has a height of 5.6 m, the height and distance of
surrounding buildings are considered 6 m, while for the building in Berlin, which is elevated by 25.6
m, the surrounding buildings are considered 25 m.
Figure 4.5. Real urban context of the case studies (left to right: Gdynia, Berlin).
Figure 4.6. Pavilion and slab urban typologies (left to right: Gdynia, Berlin).
b) Six complex hypothetical typologies: to add more complexity to a simplified Martin and March’s
typology, we apply a simple random sampling approach to select the distance of the surrounding
buildings and their heights from the target building. Table 4.4 represents the ranges of values, Tables
4.5 and 4.6 and Figures 4.7 and 4.8 include the details about building in the building network.
Table 4.4. Range of heights and distances for generating random typologies.
Gdynia case study
Berlin case study in
Building height of simulated building (m)
5.6
25.6
Range of values for height of surrounding buildings (m)
5-10
20-25
Range of values for distance of surrounding buildings (m)
5-15
15-25
76
Table 4.5. Hypothetical typology with varying heights and distances of surrounding buildings (case study
in Gdynia).
Hypothetical Urban Typology 1
Hypothetical Urban Typology 2
no.
1
2
3
4
5
6
7
8
1
2
3
4
5
6
7
8
D
5
10
15
9
15
13
8
5
6
14
7
11
9
5
11
7
H
6
10
8
9
10
6
6
8
6
7
9
6
7
6
6
6
Hypothetical Urban Typology 3
Hypothetical Urban Typology 4
no.
1
2
3
4
5
6
7
8
1
2
3
4
5
6
7
8
D
8
13
13
11
8
9
8
13
8
8
10
7
14
12
12
10
H
10
10
7
8
7
6
10
9
8
7
10
8
9
5
10
5
Hypothetical Urban Typology 5
Hypothetical Urban Typology 6
no.
1
2
3
4
5
6
7
8
1
2
3
4
5
6
7
8
D
9
13
8
10
11
6
6
14
14
11
9
6
9
10
8
7
H
5
10
5
5
5
6
5
8
7
7
8
5
10
9
5
9
Table 4.6. Hypothetical typology with varying heights and distances of surrounding buildings (case study
in Berlin).
Hypothetical Urban Typology 1
Hypothetical Urban Typology 2
no.
1
2
3
4
5
6
7
8
1
2
3
4
5
6
7
8
D
18
24
14
24
25
20
15
10
24
14
16
17
14
20
21
15
H
23
21
24
25
24
16
20
22
22
23
19
21
20
21
20
15
Hypothetical Urban Typology 3
Hypothetical Urban Typology 4
no.
1
2
3
4
5
6
7
8
1
2
3
4
5
6
7
8
D
21
23
19
18
12
14
17
19
25
10
12
23
23
20
19
25
H
21
21
20
23
20
18
23
17
24
25
17
23
20
18
16
17
Hypothetical Urban Typology 5
Hypothetical Urban Typology 6
no.
1
2
3
4
5
6
7
8
1
2
3
4
5
6
7
8
D
23
12
14
22
13
20
23
10
24
12
25
22
23
16
25
15
H
21
15
25
17
21
21
24
17
20
24
17
24
18
18
19
25
77
Figure 4.7. The building block representing Table 4.5.
Figure 4.8. The building block representing Table 4.6.
4.5. Result
Tables 4.7 and 4.8 present the IBE on the annual and winter months heat demand of the building in
various building networks compared to the stand-alone building. The annual results show variations
ranging between 1.5% and 3.6% in Gdynia case study and between 2.4% and 8.7% in the Berlin case
study. Although the height to width ratio (H/W), which defines the area’s canopy by the height of the
building and distance between the buildings, is not significantly different in the two cases, the results
indicate that the mid-rise building presents a higher IBE on heat demand compared to the low-rise
building. On the other hand, both cases experience the maximum IBE in October. However, the Berlin
case study shows a much higher maximum monthly effect of around 19.3% compared to the Gdynia
case study, which is 5.6%.
Table 4.7. IBE on monthly and annual heating demand considering various typologies compared to stand-
alone building – Gdynia case study.
IBE (%)
Jan
Feb
Mar
Oct
Nov
Dec
Annual (Winter
months)
Pavilion
4.0
2.0
2.3
4.2
2.2
2.5
2.9
Hypothetical typology 1
2.3
1.3
1.5
2.3
1.4
1.4
1.7
Hypothetical typology 2
4.6
2.8
2.8
5.6
2.9
2.8
3.6
Hypothetical typology 3
2.2
1.2
1.2
2.0
1.3
1.4
1.5
Hypothetical typology 4
4.1
2.5
2.2
5.0
2.6
2.5
3.2
Hypothetical typology 5
2.5
1.3
0.9
1.9
1.3
1.4
1.6
Hypothetical typology 6
4.0
2.0
1.5
3.5
2.4
2.5
2.6
1
2
4
5
6
7
1
3
8
2
1
2
3
4
5
7
8
6
3
1
2
3
4
5
6
7
8
4
1
2
3
4
5
6
7
8
5
1
2
3
4
5
6
7
8
6
1
2
3
4
5
6
7
8
2
1
2
3
4
5
6
7
8
1
1
2
3
4
5
6
7
8
3
1
2
3
4
5
6
7
8
4
1
2
3
4
5
6
7
8
5
1
2
3
4
5
6
7
8
6
1
2
3
4
5
6
7
8
78
Table 4.8. IBE on monthly and annual heating demand considering various typologies compared to stand-
alone building – Berlin case study.
IBE (%)
Jan
Feb
Mar
Oct
Nov
Dec
Annual (Winter
months)
Slab
1.0
2.6
3.4
8.5
3.5
0.9
3.3
Hypothetical typology 1
3.2
5.7
6.8
17.5
9.3
3.2
7.6
Hypothetical typology 2
1.0
3.0
4.5
10.6
3.8
0.9
4.0
Hypothetical typology 3
1.0
2.8
3.8
9.5
3.7
1.0
3.6
Hypothetical typology 4
1.0
3.3
5.0
11.7
4.0
0.9
4.3
Hypothetical typology 5
3.8
6.8
7.9
19.3
10.6
3.7
8.7
Hypothetical typology 6
0.8
2.3
1.9
5.5
3.0
0.7
2.4
In contrast to heating demand, the IBE results in a decrease in daylight compared to the standalone
building (Tables 4.9 – 4.12). However, the reduction of illuminance level due to IBE in the Gdynia case
study is much bigger than in the Berlin case study. In the Gdynia case study, the maximum variation
caused by IBE considering all summer months is -64.8% for the room space oriented towards the south
and -29.8% for the kitchen zone located towards the north. On the other hand, in the Berlin case study,
the maximum decrease due to IBE for the living room oriented towards the south is 35.8%, and 16.7%
for the bedroom oriented towards the north. Hence, the findings indicate that, IBE has a more
pronounced impact on illuminance levels in the low-rise building compared to the mid-rise building.
The results also show, for buildings in the northwestern Europe, south direction shows a more
considerable IBE on the illuminance level of the space. On the other hand, in both case studies, the
maximum IBE is registered in June and September for the room located in the south direction. The
building in Gdynia experiences a maximum decrease of 69.2%, while the room in Berlin shows a
maximum reduction of 37.5%. As for the room oriented towards the north, the maximum IBE results in
a maximum reduction of 30.1% in Gdynia occurring in April and July, and a reduction of 18.8% in May
and July in Berlin.
Table 4.9. IBE on daylight considering various typologies compared to stand-alone building – Room space
in the Gdynia case study.
IBE (%)
Apr
May
Jun
Jul
Aug
Sep
Summer months
Pavilion
-31.6
-32.5
-33.6
-33.1
-32.8
-33.7
-32.9
Hypothetical typology 1
-20.7
-21.3
-22.0
-21.8
-21.5
-21.9
-21.5
Hypothetical typology 2
-63.9
-62.5
-64.6
-63.2
-65.6
-69.2
-64.8
Hypothetical typology 3
-8.5
-9.3
-9.8
-9.6
-8.9
-8.5
-9.1
Hypothetical typology 4
-55.1
-55.2
-57.0
-55.8
-57.6
-60.7
-56.9
Hypothetical typology 5
-6.6
-7.3
-7.6
-7.5
-6.8
-6.4
-7.0
Hypothetical typology 6
-26.3
-26.8
-27.5
-27.2
-27.4
-28.4
-27.3
79
Table 4.10. IBE on daylight considering various typologies compared to stand-alone building – Kitchen
space in the Gdynia case study.
IBE (%)
Apr
May
Jun
Jul
Aug
Sep
Summer months
Pavilion
-8.5
-8.4
-7.8
-8.3
-7.9
-7.7
-8.1
Hypothetical typology 1
-5.1
-5.1
-4.7
-5.0
-4.7
-4.6
-4.9
Hypothetical typology 2
-3.8
-3.7
-3.3
-3.6
-3.4
-3.3
-3.5
Hypothetical typology 3
-30.1
-30.0
-29.5
-30.1
-29.8
-29.5
-29.8
Hypothetical typology 4
-25.7
-25.6
-25.2
-25.7
-25.5
-25.2
-25.5
Hypothetical typology 5
-4.7
-4.6
-4.1
-4.4
-4.2
-4.1
-4.4
Hypothetical typology 6
-6.5
-6.5
-6.1
-6.5
-6.1
-5.9
-6.3
Table 4.11. IBE on daylight considering various typologies compared to stand-alone building – Living
room space in the Berlin case study.
IBE (%)
Apr
May
Jun
Jul
Aug
Sep
Summer months
Slab
-13.7
-15.5
-16.8
-16.2
-14.9
-15.8
-15.5
Hypothetical typology 1
-33.9
-33.8
-36.9
-35.6
-35.2
-35.5
-35.1
Hypothetical typology 2
-16.9
-18.2
-19.7
-19.0
-17.5
-18.5
-18.3
Hypothetical typology 3
-15.0
-16.8
-18.3
-17.6
-16.2
-17.2
-16.8
Hypothetical typology 4
-18.6
-19.9
-21.5
-20.7
-19.2
-20.7
-20.1
Hypothetical typology 5
-34.4
-35.1
-37.5
-36.2
-35.8
-35.9
-35.8
Hypothetical typology 6
-10.5
-11.8
-12.7
-12.2
-11.3
-10.6
-11.5
Table 4.12. IBE on daylight considering various typologies compared to stand-alone building – Bedroom
space in the Berlin case study.
IBE (%)
Apr
May
Jun
Jul
Aug
Sep
Summer months
Slab
-13.6
-15.2
-15.0
-15.1
-14.0
-7.7
-13.4
Hypothetical typology 1
-16.6
-18.8
-18.7
-18.8
-17.1
-9.4
-16.6
Hypothetical typology 2
-14.5
-16.2
-15.9
-16.0
-14.9
-8.3
-14.3
Hypothetical typology 3
-16.8
-18.8
-18.7
-18.7
-17.3
-9.7
-16.7
Hypothetical typology 4
-13.2
-14.2
-13.8
-14.0
-13.3
-7.6
-12.7
Hypothetical typology 5
-14.7
-16.5
-16.4
-16.4
-15.2
-8.3
-14.6
Hypothetical typology 6
-12.5
-13.7
-13.2
-13.4
-12.7
-7.2
-12.1
Results show that the IBE on lighting illustrates more significant variation compared to the heat demand.
Also, depending on the location of the building and its height, the IBE can be crucial for heat demand
and illuminance level. In line with previous analyses, various orientation results in various IBE on the
natural lighting capture of the spaces.
4.6. Discussion
The main questions that motivated this study are: 1) How sensitive is the annual and monthly heating
demand and daylight performance of the selected target buildings located in various urban typologies
compared to stand-alone building? 2) How different is the results compared to previous studies? 3) How
can this information support renovation projects?
80
To answer the above questions, this study builds upon previous research and expands the simplified
urban typologies to include randomly selected heights and distances of surrounding buildings in a nine-
block network. The study makes an implicit contribution to the extensive research on the shading effect
of various urban typologies. Specifically, we estimate the IBE on building energy performance namely,
the heating demand and illuminance level in specific zones of the buildings, considering a building
network generated by randomly selected heights and distances in the surrounding area of a target
building. By using this approach, we can capture the effect of shading from various urban forms, rather
than focusing on a single, predetermined hypothetical typology. Our findings demonstrate that IBE can
have a significant impact on simulation outcomes, emphasizing that it is necessary to consider the effects
of current and future urban developments in renovation projects. Regardless of the orientation, height
and window-to-wall ratio of the target building, the IBE on lighting has a major impact which requires
a more systematic analysis rather than only examining case studies in empirical settings. In renovation
studies where visual comfort of the building is an important factor for renovation assessment, integrating
the IBE studies for natural light assessment of the building seems crucial.
More specifically, the simulation results of this study reveal a maximum annual increase of 8.7% in
energy demand and a maximum decrease in daylight by 64.8%. Additionally, the study found that the
IBE is more pronounced in mid-rise building regarding heat demand and more prominent in low-rise
buildings and the south-facing zone of a building, regarding the natural daylight capture. One of the
limitations of this study is that it is challenging to compare the results of this research with the findings
of previous studies for two main reasons. Firstly, most earlier studies were conducted in buildings
located in the US and China. Therefore, their results may not be comparable to European cities due to
differences in urban characteristics and building locations, resulting in varying solar angles. Secondly,
Unlike the studies conducted in Europe where the IBE is generally calculated for the total energy
demand comprising heating, cooling, and electricity, our study solely focuses on heat demand and
illuminance level. For instance, Strømann-Andersen and Sattrup [14] conducted a study in Copenhagen,
Denmark, which showed that the total annual energy consumption (including heating, cooling, and
electricity) in office and residential buildings varied up to +30% and +19%. In another research
conducted on two office buildings in Perugia, Italy, a 14.7% and 7% variation in heating, cooling, and
electricity demand was observed for buildings with north-east and south-west facing, respectively, along
with a higher peak load for the IBE [26]. The findings of our study indicate that the IBE on annual
heating demand is limited to a maximum variation of only 8.7%. It suggests that the cooling and
electricity demand are more susceptible to the influence of IBE. This confirms the findings by Pisello et
al. [26] which shows that building electricity demand is more influenced by IBE than heating and
cooling consumption. On the other hand, although the annual heat demand variation is up to 8.7%, the
maximum monthly heating demand shows a 19.3% variation due to IBE, which is in line with the
findings of the earlier research [26]. In addition, our findings indicate that there is a maximum annual
decrease of 64.8% in daylight capture in the building, although the degree of variation depends on the
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orientation of the selected zones. Specifically, both buildings show a higher reduction in south-facing
zones, almost twice that of north-facing zones. This confirms the finding of Pisello et al. [26], which
suggest that increasing building density can result in lower lighting energy use for north-facing buildings
than for south-facing ones. Additionally, our study shows that the building in Gdynia, which has a lower
height, experiences a much higher reduction of daylight for both orientations (north and south) than the
building in Berlin, which has a higher elevation. This finding also supports the finding by Pisello et al.
[26], that lower floors are more influenced by the IBE.
To the best of our knowledge, there is only one previous study that similarly evaluate the sensitivity of
BPS to variation of urban typologies. However, this study is focused on the buildings in hot-arid area of
Tehran [22]. Therefore, the current study is unique as it focuses on a different geographical context.
However, applying a parametric model and generating big number of hypothetical typologies with the
same approach is not in the scope of this study and is envisaged for future research. Additionally,
previous research on this topic aimed to provide insights for urban planners and policymakers and had
implications for overall urban planning and quality of life in neighborhoods rather than studying the IBE
in the energy efficiency at the individual building level. This research focuses on the shading effect of
the urban built environment on the BPS of individual buildings in renovation projects. As a result,
suggestions for mitigating IBE should support architects, designers, and engineers in renovation of
individual buildings, to enhance building modeling accuracy and applying passive designs to balance
solar gain in the building.
Figure 4.9. Proposed workflow for IBE integration in renovation projects.
Figure 4.9 portrays a proposed workflow to integrate IBE on heating, cooling, and daylight in renovation
projects to reduce its impact. We stress the importance of conducting a comprehensive analysis of
surrounding buildings before initiating any building renovation project. An evaluation of the potential
impact of IBE on building performance for renovation scenarios and as-built building performance is
required. While this study does not examine the facade material of the neighboring buildings, an
investigation of the structure of neighboring buildings including façade material is essential. An
assessment of specific characteristics, such as building height and orientation, should be conducted
based on the renovation objective. For instance, when visual performance is a renovation goal, a low-
rise building is more susceptible to shading effects than a mid- or high-rise building.
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The findings should be communicated with stakeholders involved in the renovation project, including
the design team and contractors, to enable them to take appropriate measures for mitigating the impact.
For achieving a more precise understanding of the thermal and visual behavior of a building, it is
necessary to develop a building model that integrates the urban built environment, especially in densely
populated areas. Furthermore, when renovating a building in a less densely area, it is important to
consider the future urban development in the region to meet the efficiency expectations in long-term.
Based on the recommendations of experts and the results of IBE-integrated simulations, appropriate
materials and techniques, such as installing or removing shading devices, changing the window-to-wall
ratio in the building, modification of glazing should be utilized during the renovation process to ensure
that the primary objective of building renovation, which is to maintain the building’s long-term
performance and provide a high-quality environment for occupants, is achieved [35].
While this study provides beneficial insights for the renovation community about the inter-building
effect on building performance, it is essential to acknowledge its limitations. Although certain
simplifications were necessary for computational feasibility and to have a clear scope in the study, these
simplifications may affect the accuracy of our results. Therefore, it is important to be cautious about
generalizing the research findings. The first limitation is that the building network is simplified.
Surrounding buildings could be more detailed with information, such as facade material and glazing.
Additionally, the study only examines six hypothetical building networks, while investigating the IBE
of more typologies can provide a more realistic insight of the IBE potential on building performance.
The second limitation of the study is the uncertainty in other input datasets applied in the simulation,
such as the target building model under renovation and the weather dataset used in the simulation
process. This can influence the reliability of the results. Finally, the findings are based on two residential
case studies in Europe, and the results of the study cannot be generalized.
Future research should explore a broader range of hypothetical urban typologies to evaluate building
performance under various alternatives. Increasing the complexity of the building network by
incorporating more information about the buildings, such as facade material and glazing, and adding
more complexity to the target building by incorporating more detailed information regarding shading
devices would be of interest. Furthermore, regarding the visual comfort of the building, including
information about occupant’s consumption patterns and integrating occupants’ input about the influence
of the building network could be valuable. While sensitivity analysis of various morphological factors
that generate the building network on the building performance has been previously studied, future
research can further investigate other parameters, including building orientation, glazing parameters,
building materials, user behavior, occupancy patterns, and climate conditions. Lastly, future studies can
explore the impact of IBE on various renovation scenarios and evaluate this approach in diverse
European cities with varying climate zones using up-to-date weather datasets. Within the framework of
renovation studies, integrating optimization algorithms to identify the best renovation scenarios while
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considering IBE, such as incorporation of shading devices, considering specific zones to daylight for
occupants, can be explored further.
4.7. Conclusion
Due to the intricate interplay between a building and its surrounding structures, this research is motivated
by the limited knowledge within renovation projects on how sensitive building performance is to this
factor in European cities. To bridge this gap, this research presents a more complex urban typology,
compared to previous studies, by randomly selecting heights and distances for surrounding buildings.
The primary aim of this research is to examine the sensitivity of heating demand and daylight capture
to surrounding buildings compared to a standalone building. To achieve this, energy demand and
daylight were estimated for two residential buildings, in Gdynia and Berlin. Our results indicate that,
simulation outcomes can be highly sensitive to the shading effect of surrounding buildings. Therefore,
in renovation projects, an investigation of such effects due to current and possible future urban
developments is required. This information provides energy experts and designers valuable insights to
make informed decisions.
Integrated models were created by coupling EnergyPlus and R to evaluate the building’s year-round
thermal and visual behavior. The results of performance simulation in the two residential buildings
within the hypothetical urban contexts indicate that the case studies in Gdynia and Berlin show a
maximum annual increase of 3.6% and 8.7%, and a maximum monthly increase of 5.6% and 19.3%, in
heat demand, respectively. The annual daylight decreases by a maximum of 64.8% in the Gdynia case
study (low-rise building) and by a maximum of 35.8% in the Berlin case study (mid-rise building). In
both cases, the south-facing zone experiences almost double the amount of daylight decrease compared
to the north-facing zone, and the low-rise building is more affected by IBE regarding daylight.
Based on these findings and the potential of renovation projects to mitigate the IBE, we suggest a
workflow for integrating IBE into renovation projects. Through this, we aim at facilitating more accurate
and realistic decision-making, improving building performance and enhancing inhabitant comfort.
Acknowledgments
This research project is funded under the European Union’s program H2020-NMBP-EEB-2018, under
Grant Agreement no 820553. Views and opinions expressed are however those of the author(s) only and
do not necessarily reflect those of the European Union. Neither the European Union nor the granting
authority can be held responsible for them.
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5. Discussion and Conclusion
The objective of this dissertation was to provide an understanding of how integrating surrounding
geospatial and environmental data can support building renovation (the path through the research
depicted in Figure 5.1). To this aim, a knowledge framework has been developed to capture the key
concepts from the geospatial domain that influence building renovation in various use cases. Following
that, the sensitivity of building performance simulation to some of these factors has been investigated,
through explorative analyses. This section discusses the findings, limitations, and potential future
research and concludes the thesis.
Figure 5.1. The path through the dissertation.
RQ1. How can an ontology-based knowledge framework, representing concepts from geospatial
domain, support building renovation?
Chapter 2 of the dissertation presented the development of a knowledge framework, represented in the
form an ontology, which maps the concepts, relations, and processes from the geospatial domain that
support building renovation in different use cases. The ontology serves as a reference for various expert
groups involved in building renovation projects. The concepts included in the ontology were derived
from literature review, brainstorming sessions with experts, investigating maps of real demonstration
sites, and visits to construction sites. The ontology was evaluated by conducting a workshop with experts
involved in building renovation projects, who are the intended end-users of the ontology. Based on the
ontology, a prototype has been developed and used in the workshop to present the ontology to facilitate
user interaction. The prototype has been used to collect relevant geospatial data for site planning use
case of a renovation case study in Berlin, Germany, and based on the collected data, insights for planning
of the renovation project has been proposed. The experts in the validation workshop highlighted that
V
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engineers and energy experts involved in renovation projects often lack awareness about the benefit of
such data in building renovation projects and are limited by the existing software and tools at their
disposal. The findings of this research demonstrate that such an ontology support renovation projects to
a great extent, particularly in the planning phase.
RQ2. What are the implications of using different typical-year weather datasets for building energy
performance analysis in the context of building renovation, and how can this knowledge be used to
inform the development of better weather standards and policies?
For accurate building performance simulation, the weather datasets used as input should represent the
long-term conditions of the area and cover the building’s entire lifespan. Typical-year weather datasets
generated from long-term historical weather data are commonly utilized in building energy modeling.
Chapter 3 was intended to shed light on the sensitivity of building performance simulation in renovation
projects to the various typical-year weather datasets. This study explored the impact of weather data
generated from different periods, methodologies, and sources on simulated building heat demand. The
study’s findings revealed that annual heating demand varies between -4.2% and 13.5% in three cases in
Europe due to various typical-year weather datasets. The findings of Chapter 3 confirm the results of
other studies such as [1] and [2] regarding potential variations in energy demand due to selection of
various typical-year weather datasets. Following that, a synthetic weather dataset has been developed
by modifying the existing typical-year weather data with average values of actual dry-bulb temperature.
The findings indicated that the proposed weather dataset provides a more accurate estimation of energy
consumption, with a maximum overestimation of only 6% compared to the average actual energy
consumption. Also, the findings of this research indicated that there is no advantage in using typical-
year weather data derived from more recent datasets for estimating heat demand.
This study highlighted the sensitivity of building energy performance simulation to weather data
selection and the importance of carefully selecting the most appropriate weather dataset. In addition,
based on the findings of this research, chapter 3 highlights the key role that policymakers and
standardization organizations play in developing new weather standards and guidelines that represent
the realistic long-term conditions of an area and consider the challenges that climate change causes.
RQ3. What are the implications of considering shading effect of surrounding buildings on building
performance simulation, and how can this knowledge be used to allow better building design in
renovation projects?
Many studies have examined the inter-building effect of various urban typologies to provide insights for
possible planning in the development of energy-efficient urban structures (some of these studies are
summarized in Section 4.3). The research presented in chapter 4 is focused on the sensitivity of building
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performance simulation to the shading of surrounding buildings in the renovation of individual
buildings.
To this end, hypothetical complex urban typologies were developed, built upon the simplified and
uniform Martin and March typologies [3], by randomly selecting heights and distances for the
surrounding buildings for two residential case studies in Europe. The building performance simulation
of the building has been performed using the integrated building model and its surrounding buildings.
Following that, the results of the simulation has been compared with the results of the simulation of the
stand-alone building. For the selected case studies, the simulation results show a maximum increase of
8.7% for annual heat demand and a maximum decrease of 64.8% for daylight capture when considering
the building in the hypothetical urban typologies, compared to a stand-alone building. In addition, a
nearly double daylight reduction for the south-facing zone is demonstrated compared to the north-facing
zone.
The findings of this research can help engineers and designers of renovation projects to make informed
decisions when selecting the renovation scenarios. As the results show the dramatic influence of the
shading effect of neighboring buildings on building performance simulation, particularly in daylight
capture, the dissertation proposed a workflow for incorporating such information into the renovation
process.
5.1. Reflections
The main goal of this dissertation was to contribute to the EU’s deep renovation as one of the main
targets of energy efficiency in Europe [4]. Based on the insights gained through this research, deep
renovation of individual buildings is not achievable without considering the building in connection with
its environment and the urban context in which it is located. Discussion on the dissertation is carried out
in the form of reflections on conceptual, practical and policy aspects.
Reflection 1. A prerequisite for integration of two domains is to have a clear understanding of the
complexity of the domains. The discipline of Advanced Engineering Informatics tries to understand the
complex behavior of systems and data models and to enhance the collaboration of experts in
interdisciplinary situations [5]. It provides means for formalizing engineering activity and domain
knowledge explicitly and suggests accounting for the purpose and context [5]. The dissertation has relied
on this theory to formalize knowledge in the form of an ontology. This ontology represents the concepts
from geospatial domain which are beneficial for the purpose of individual building renovation and
integrates the domain experts in validation of this knowledge framework. Therefore,
1. It provides a frame of reference for engineers in renovation projects to communicate with each other
about the required geospatial data. The engineers can use it for collecting relevant geospatial datasets
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or making the existing datasets reusable in various renovation projects. Although some engineers
incorporated surrounding geospatial data in renovation projects, particularly in the planning phase
[6], there is currently no conceptual framework to formalize such knowledge. The proposed ontology
is sought to be this basis. The workshops conducted for validating the ontology demonstrated that
engineers involved in renovation projects often neglect the potential of geospatial data. This may be
due to limited awareness about the benefit of such data, lack of access to tools that help integrate
such data in building models, and the expertise required for working with geospatial datasets. The
ontology developed in this dissertation provides such a frame of reference.
2. This standard knowledge framework can then be applied in practice to implement a data collection
dashboard, which facilitates easier access to such datasets. One of the impacts of this research was
the implementation of such a platform. The developed ontology has been utilized as a basis for the
implementation of a prototype in an EU research project called BIM-Speed [7], to collect the
necessary geospatial datasets for various use cases within renovation projects [8]. Details about this
prototype is provided in Figure 2.7 of Section 2.5.4.
3. This ontology is a basis for creating a consistent machine-readable data model, that can be used in
data integration in practice. The integration of BIM and GIS data is a popular topic in the construction
industry, although it is not widely discussed in the context of renovation projects. Based on this
ontology, a representational model, built upon existing data models such as CityGML [9] and gbXML
[10] can be envisaged [11].
Reflection 2. Although previous studies have explored the effect of external datasets on building
performance simulation, there is limited knowledge about renovation projects. This knowledge is
significant for renovation projects since their primary objective is to improve energy efficiency and
occupant’s comfort. Engineers and energy experts of renovation projects usually employ weather
datasets available through weather data sources without enough scrutiny about the accuracy and
applicability of the weather data for building performance simulation. This research highlights the
substantial effect of weather data as one of the input parameters in building performance simulation of
renovation projects. On the other hand, previous research is mainly focused on investigating the shading
effect of surrounding buildings on building performance simulation to provide insights for urban
planners in determining efficient urban forms (summarized in Section 4.3). This research contributes to
the body of knowledge by shedding light on the significance of shading effect of surrounding buildings
on building performance simulation in renovation of individual buildings by investigating the effect of
hypothetical complex urban typologies. The insights provided by simulation scenarios demonstrate how
overlooking the uncertainty related to external parameters in the simulation process can influence the
estimation of building performance. These insights assist practitioners and policymakers in decision-
making and implementing more optimized solutions.
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Reflection 3. The findings of this research demonstrate that the methodology applied for developing
weather datasets for building performance simulation is outdated. Therefore, it is essential to reevaluate
the current practice of generating typical-year weather datasets. Also, building performance simulations
using typical-year weather datasets generated from recent-years historical weather data, especially in
Europe is limited [1], [12], [13], and requires more scrutiny. The dissertation calls on standardization
organizations to develop updated weather standards and methodologies that accurately represent the
climate conditions, consider climate change effects, and are compatible with the climate zones. This is
particularly important for renovation projects since examining building performance in the case of
severe weather events such as heat wave help in designing renovation scenarios that can adapt to these
conditions in the future. Lastly, a standard workflow and guidelines that help energy experts select the
most accurate and appropriate weather data for building energy simulation is substantially helpful. This
research suggests that investigating climate condition and how it is addressed in the building analysis
requires a deeper scrutiny. Therefore, it urges researchers to prioritize research focus on this topic.
Reflection 4. This dissertation contributes to interdisciplinary research by bridging the gap between the
renovation domain and the geospatial field. It challenges the current practice in renovation projects and
research that focuses only on increasing the accuracy of the building model to enhance the accuracy of
the building performance simulation. It encourages other researchers to explore synergies between
various fields that can lead to innovative insights.
5.2. Limitations and future research
This dissertation provided insights for engineers about integrating geospatial and environmental data
into the building in renovation projects. However, in each of the studies, various limitations are
mentioned in the discussion sections of respective chapters. Nevertheless, a summary of some
limitations of the dissertation is provided below.
Firstly, developing an ontology is an ongoing activity. There are always opportunities for improvement
in the selection of use cases, concepts, and the relation between them. Therefore, the proposed ontology
should be revisited and maintained regularly to confirm its relevance over time. It is also important to
promote the ontology for adaptation by developing representational models based on that, to use it in
data integration in practice. Another limitation of the proposed ontology is that the validation of the
ontology is conducted by a limited number of experts in the workshops. Using other approaches for
validation, such as distributing surveys and questionnaires for collecting feedback, could help increase
the number of involved experts. However, the conducted workshop led to an open-ended discussion,
which was not possible through using surveys.
Although the ontology could provide information about the required geospatial datasets for various use
cases within renovation projects, the description of how these datasets is beneficial in practice for a
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specific case study is only provided for one use case, namely, the site planning and for one renovation
case study in Europe. However, it is essential to document the potential influence of proposed concepts
from the geospatial domain in other suggested use cases, such as acoustic and indoor air quality comfort
analysis of the building.
One of the aspects that has not been discussed in the thesis is the appropriate distance around the building
within which various geospatial datasets should be collected. Depending on the goal, scope, and use
case, the geospatial features at specific distances around the building are effective. For example, if
investigating the microclimate and shading effect of surrounding buildings is required, the geospatial
features in the immediate proximity of the building are adequate. On the other hand, if the goal is to
improve accessibility, a larger radius of data collection would be required to assess transportation
networks, infrastructure, and so on. Therefore, the specific distance to collect and investigate geospatial
data should be determined based on the unique objectives and needs of the renovation project. This can
be conducted by a comprehensive analysis to identify the relevant factors and their spatial extent that
are likely to have an impact on the desired outcomes.
Moreover, this dissertation investigates the influence of various external factors on the building
performance simulation. However, the findings of these studies should not be generalized, as there are
several limitations in setting up these case studies, due to the time constraint imposed on the research.
The selected hypothetical urban contexts are very limited and are chosen to represent some of the
possible complex urban structures. Also, for investigating the impact of weather data on building
performance simulation, a limited number of weather datasets are selected. Building performance
simulation considering other hypothetical urban structures or typical-year weather datasets, may lead to
different results. Therefore, to broaden the knowledge in this field, potential future research is to explore
more what-if conditions in developing the scenarios for urban typologies and weather datasets. Future
research can also extend the external parameters to other surrounding obstacles, such as trees, and
investigate the effect of climate conditions on building performance simulation along with the shading
and microclimate effect. Furthermore, the typical-year weather datasets used in the simulations were
obtained from existing data sources. Typical-year weather datasets are sought to be generated from long
period of historical weather data, to cover the building’s lifetime. However, there is lack of such weather
datasets generated from recent years actual weather data that cover such long period of record.
Therefore, there is a chance that the selected typical-year weather datasets in this study do not properly
represent the real climate condition of the area in the long-term.
Moreover, in building performance simulation of selected buildings, this research did not take into
account the potential inaccuracies in the detailed building model, the uncertainties in the weather data
collection, and the sensors used to collect such data. Therefore, the simulation results may include such
inaccuracies as well. On the other hand, this research only focused on the heat demand of the building
when studying the building's performance. However, it is essential to investigate the effect of external
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parameters on the predictive cooling demand of the building due to the increase in temperature expected
by climate change effects. The results of such analysis can be helpful in the decision-making of
renovation scenarios. Installing appropriate HVAC systems, incorporating passive design to optimize
natural ventilation, and integrating shading devices are some of the examples to be considered in the
renovation to increase the energy efficiency of the building and inhabitant's comfort.
Furthermore, this research is not focused on studying the discrepancy in the predicted energy
performance of the building and the real energy use. Therefore, future research can investigate the
performance gap while considering the real-life complexities via the integration of external parameters
in the predicted building performance simulation and comparing it with the real energy use of the
building. This research investigates the effect of external factors on building performance of renovation
projects, as the case studies are from the renovation cases and represent the particularities of such
projects. However, the findings of this research can also be beneficial for constructing new buildings.
Also, the focus of this research is on residential buildings. Although some of the findings of this research
can be extended to other types of buildings, such as offices, it is crucial to take into account the
particularity of such buildings and the urban contexts and environmental conditions of such areas that
make them different from residential areas. Future research can investigate other use cases in
commercial building renovation that are affected by surrounding geospatial and environmental features.
5.3. Endnote
The motivation of this dissertation was to support deep renovation in Europe. The research has
significant implications for building renovation projects in theory, practice, and policy. The theoretical
value of the dissertation is the knowledge framework represented in the form of an ontology, which can
be used as a frame of reference for renovation engineers and experts to communicate about the benefit
of geospatial data in renovation projects. The practical implication of the thesis is the prototype
developed based on this ontology to collect geospatial features required for various use cases in
renovation. Also, the insights provided through analytical studies shed light on the sensitivity of building
performance simulation to the selection of various weather datasets and the shading effect of
surrounding buildings. Therefore, the thesis encourages engineers and energy experts in building
renovation to broaden their knowledge about the influence of external factors on building performance
simulation to integrate real-life complexities into the building models.
This dissertation has two policy implications. Firstly, it highlights that, at the EU level, there is no
adequate research on weather data generation and standardization for building performance simulation
and modeling. As existing methodologies for developing typical-year weather data appear obsolete, and
climate change has forced meaningful challenges, it is crucial to have new guidelines that help in
developing new approaches for generating weather datasets that represent the climate of an area and, at
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the same time, consider the climate change complexities. Secondly, the developed ontology offers a
basis for developing standard data models that could facilitate the future integration of BIM and GIS
data for building renovation.
This research fits well in the existing body of knowledge in renovation studies, where deep renovation
is expected. It identifies crucial topics that are often overlooked in renovation projects yet have
significant potential to improve the accuracy of various analyses in the renovation workflow.
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5.4. References
[1] M. Grudzińska and E. Jakusik, “The efficiency of a typical meteorological year and actual
climatic data in the analysis of energy demand in buildings.,” Building Services Engineering
Research and Technology, vol. 36(6), pp. 658–669, 2015.
[2] M. Hosseini, A. Bigtashi, and B. Lee, “Evaluating the applicability of Typical Meteorological
Year under different building designs and climate conditions.,” Urban Clim, vol. 38, 100870,
2021.
[3] L. Martin, L. A. Martin, and L. March, Urban space and structures. Cambridge University Press,
1972.
[4] BPIE (Buildings Performance Institute Europe), “Deep Renovation: Shifting from exception to
standard practice in EU Policy.,” 2021.
[5] T. Hartmann and A. Trappey, “Advanced Engineering Informatics - Philosophical and
methodological foundations with examples from civil and construction engineering,”
Developments in the Built Environment, p. 100020, 2020, doi: 10.1016/j.dibe.2020.100020.
[6] Ö. Göçer, Y. Hua, and K. Göçer, “A BIM-GIS integrated pre-retrofit model for building data
mapping,” Build Simul, vol. 9, no. 5, pp. 513–527, 2016, doi: 10.1007/s12273-016-0293-4.
[7] “BIM-Speed EU Horizon 2020 Project.” [Online]. Available: https://www.bim-speed.eu/en
[8] M. Daneshfar, E. Pascual, A. Breitwiller, and J. Rabe, “Deliverable Report (D 1.4); IT solutions
to couple environmental, surroundings and weather data to BIM,” 2020.
[9] T. H. Kolbe, “Representing and exchanging 3D city models with CityGML,” Lecture Notes in
Geoinformation and Cartography, no. September, pp. 15–31, 2009, doi: 10.1007/978-3-540-
87395-2_2.
[10] “About gbXML.” [Online]. Available:
https://www.gbxml.org/About_GreenBuildingXML_gbXML
[11] C. Métral, R. Billen, A. F. Cutting-Decelle, and M. Van Ruymbeke, “Ontology-based approaches
for improving the interoperability between 3D urban models,” Electronic Journal of Information
Technology in Construction, vol. 15, no. February, pp. 169–184, 2010.
[12] T. Hone, WK. Chang, and HW. Line, “A Sensitivity Study of Building Performance Using 30-
Year Actual Weather Data.” 2013.
[13] G. Evola, V. Costanzo, M. Infantone, and L. Marletta, “Typical-year and multi-year building
energy simulation approaches: A critical comparison,” Energy, vol. 219, 11959, 2021.
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Appendix A: Candidate’s contribution and co-authorship.
This dissertation was written in an accumulative format, including three peer-reviewed
publications as below:
•Paper 1 (Status: Published), Chapter 2:
Daneshfar, M., Hartmann, T., & Rabe, J. (2022). An ontology to represent geospatial data to
support building renovation. Advanced Engineering Informatics, 52, 101591,
https://doi.org/10.1016/j.aei.2022.101591.
Authors’ contributions and activities:
Maryam Daneshfar (main author): Conceptualization and formulation of research goals, Investigation,
Validation, Software use, Writing
Timo Hartmann, Jochen Rabe: Conceptualization and Review
•Paper 2 (Status: Submitted), Chapter 3:
Daneshfar, M., Hartmann, T., & Amorocho, J. A. P., Is it fundamental to examine the weather data for a
reliable building energy simulation? A comparative study with different weather datasets. Building
Simulation.
Authors’ contributions and activities:
Maryam Daneshfar (main author): Conceptualization and formulation of research goals, Investigation,
Simulation, Writing
Timo Hartmann, Jerson Alexis Pinzon Amorocho: Conceptualization and Review
•Paper 3 (Status: Submitted), Chapter 4:
Daneshfar, M., Hartmann, T., The Inter-Building Effect (IBE) in Evaluating Building Performance of
Renovation Projects: The Case of European Cities. Building and Environment.
Authors’ contributions and activities:
Maryam Daneshfar (main author): Conceptualization and formulation of research goals, Investigation,
Simulation, Writing
Timo Hartmann: Conceptualization and Review
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Appendix B: The OWL file of the ontology developed in
Protégé.
Object View
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Process View
