Maryam Daneshfar, Timo Hartmann
The Inter-Building Effect (IBE) in Evaluating
Building Performance of Renovation Projects
The Case of European Cities
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Date of this version
October 2023
This version is available at
https://doi.org/10.14279/depositonce-18903
Citation details
Daneshfar, Maryam; Hartmann, Timo (2023). The Inter-Building Effect (IBE) in Evaluating Building
Performance of Renovation Projects: The Case of European Cities, Technische Universität Berlin, preprint,
https://doi.org/10.14279/depositonce-18903.
Submitted to journal Building and Environment (E-ISSN: 1873-684X, P-ISSN: 0360-1323).
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1
The Inter-Building Effect (IBE) in Evaluating Building
Performance of Renovation Projects: The Case of
European Cities
Maryam Daneshfar, Timo Hartmann
Technische Universität Berlin; Sek. TIB 1 - B13; Gustav-Meyer-Allee 25; 13355 Berlin; Germany
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 9-block building network. The heat demand, peak load, 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, a
maximum increase of 11.7% in peak load, 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
peak load, 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; Inter-Building Effect (IBE); building renovation; shading effect of
surrounding buildings
2
1. 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)
[6][7].
Previous studies incorporated the urban morphology, characterized by building density, height,
direction, and typology [8][9][10][11][12][13][14], within various climate zones [6][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 [7]. 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 [16].
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) or extended it to limited
complexities [16]. 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 9-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
3
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 2 provides a literature review on the topic and describes in
detail the contribution of this article. Section 3 describes the methodology applied for the analysis.
Section 4 presents the results, while Section 5 discusses the findings. Lastly, we conclude the paper in
Section 6.
2. Literature Review
The shading effect of surrounding buildings on building energy performance has been studied
extensively [7][17]. There is a consensus about the significance of urban form impact on energy use.
However, the magnitude of its influence is debatable [17]. 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 1) [17]. For a more
detailed description of each of these measures, refer to [17]. Regarding typologies, which refer to
representation of a group of buildings [18], many studies applied hypothetical urban layouts following
Martin & March’s prototype [18], including slab, pavilion, and courtyard (Figure 1). Other studies
considered real urban structures representing the actual development of an area [18], [19].
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 [14]. 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 [14]. Other studies are also focused on
studying best urban planning approaches for generating urban forms which result in lower urban energy
use [10], [20]– [26].
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 [25]. 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 1.
4
Figure 1. Urban form measures - authors' representation adopted from [16], [17].
Table 1: Selected studies of IBE effect in building energy performance.
Reference
Building Type / Area of Study
Findings of the Study
[27]
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%.
[28]
Hypothetical 9-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.
[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.
5
- 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 9-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.
[20]
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.
[21]
1600 urban configurations
considering various density,
layout and building form of
surrounding buildings in hot-arid
climate of Iran
- Shading effect cause an improvement in cooling
demand by 10%.
- The study generates best urban configurations
with highest ventilation possibilities and lowest
cooling demand.
[22]
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.
[23]
Office building with different
scenarios including
conventional, cool, and
thermochromic coatings applied
on the roof or on the building
facades, also under several
climate change scenarios in
Toronto, Canada.
- Thermochromic paints can decrease the cooling
demand by 1.7%. therefore, it is beneficial in
reducing the IBE effect.
[24]
Typical 8-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.
6
[sr: square radian]
[25]
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.
[26]
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 neighboring
obstructions.
2.1.Research gaps and contributions
Table 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 9-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 building 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:
• How sensitive is the annual and monthly heating demand, heating peak load and daylight
performance of selected target building under renovation located in various urban typologies
compared to stand-alone building?
• How different is the results compared to previous studies?
• How can this information support renovation projects of these case studies?
7
Section 3.1 describes in detail the methodology we applied to develop this research.
3. Method and Material
Figure 2 illustrates the methodology applied in this study. The first step is to identify the buildings for
investigation. The Two selected residential buildings are typical multi-family residential buildings in
Gdynia and Berlin. 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 three metrices namely heat demand,
heat peak load, 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 peak load as well as indoor natural lighting.
Figure 2. Methodology of the research.
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. Buildings’ 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,
8
a Typical Meteorological Year (TMY) weather data is applied which is retrieved from EnergyPlus
weather sources.
3.1.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: 17 c, 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 3).
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 2 and 3.
Table 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
9
Table 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
Figure 3. Placement of daylight controls (left to right: Gdynia, Berlin).
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 [36]. 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 3). Figure
4 shows the orientation of each space in the building that include the lighting control.
10
Figure 4. Daylight evaluation spaces in the case studies left to right Gdynia and Berlin case studies.
3.2.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. [9]. 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/peak load/illuminance level of the control building within the building network
for the month i.
𝐻𝑃𝐼𝑠,𝑖: heat demand/peak load/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:
a) Simplified Martin and March’s typology: looking at the real urban context of the buildings
(Figure 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 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.
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 represents the ranges
11
of values, Tables 5 and 6 and Figures 7 and 8 include the details about building in the building
network.
Figure 5. Real urban context of the case studies (left to right: Gdynia, Berlin).
Figure 6. Pavilion and slab urban typologies (left to right: Gdynia, Berlin).
Table 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
Table 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
12
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
Figure 7. The building block representing Table 5.
Table 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
Figure 8. The building block representing Table 6.
13
4. Result
Tables 7 and 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
from 1.5% to 3.6% in the 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 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
Table 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
14
Figure 9 depicts the IBE on peak load during the winter months in different typologies compared to a
stand-alone building. The results indicate that the highest discrepancy in peak load occurs in October
in both case studies. However, like heat demand, the IBE on peak load is higher in the Berlin case study,
with a maximum value of 11.7%, whereas in Gdynia, it is 4.1%.
Figure 9. IBE on monthly peak load considering various typologies compared to stand-alone building.
In contrast to heating demand and peak load, the IBE results in a decrease in daylight compared to the
standalone building (Tables 9 - 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 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 July, and a reduction of 18.8% in July and May
in Berlin.
Table 9. IBE on daylight considering various typologies compared to stand-alone building – Room space
in 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
15
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
Table 10. IBE on daylight considering various typologies compared to stand-alone building – Kitchen
space in 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 11. IBE on daylight considering various typologies compared to stand-alone building – Living room
space in 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 12. IBE on daylight considering various typologies compared to stand-alone building – Bedroom
space in 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
16
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
and peak load. Also, depending on the location of the building and its height, the IBE can be crucial for
heat demand, peak load, and illuminance level. In line with previous analyses, various orientation results
in various IBE on the natural lighting capture of the spaces.
5. Discussion
The main questions that motivated this study are: 1) How sensitive is the annual and monthly heating
demand, heating peak load 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?
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 9-
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, peak load, 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, a maximum increase of 11.7% in peak load, 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 peak load 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
17
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 et al. [20] 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 [25]. 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. [25] 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 17.5% variation due to IBE, which is in line with the findings of the earlier research
[25]. 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 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. [25], 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. [25], 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 [21]. 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, by suggestions for enhancing building modeling accuracy and applying passive
designs to balance solar gain in the building.
Figure 10 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
18
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 mid-rise building.
Figure 10. Proposed workflow for IBE integration in renovation projects.
The findings should be communicated with pertinent stakeholders, including the design team and
contractors involved in the renovation project, 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 sparsely populated
area, it is important to consider the future urban development in the vicinity to meet long-term
expectations. 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 [34].
While this study provides beneficial insights for the renovation community on 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 including 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 acquisition of the IBE potential on building performance. The second
limitation of the study is the uncertainty in other input datasets, such as the target building model under
renovation and the weather dataset used in the simulation process, which can influence the reliability
19
of the results. Finally, the findings are based on two residential buildings in Europe and the results of
the study cannot be generalized to all residential buildings.
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 individual building under study 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 considering IBE, such as incorporation of shading devices,
considering specific zones to daylight for occupants, can be explored further.
6. 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, peak load,
and daylighting to surrounding buildings compared to a standalone building. To achieve this, energy
demand, peak load, and daylight were estimated for two residential buildings, in Gdynia and Berlin.
Our results indicate that regardless of the most significant morphological parameters, 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 this cross-regional analysis of hypothetical urban contexts
in two residential buildings 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 maximum variation in peak load is a monthly increase of 4.1% and 11.7%,
20
respectively. However, 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 for renovation strategies 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.
21
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