Integrating courtyard microclimate in building performance to mitigate extreme urban heat impacts

1 Integrating courtyard microclimate in building performance to mitigate extreme urban heat impacts Jesus Lizana a,b,c Victoria Patricia López-Cabeza a Renaldi Renaldi b,c Eduardo Diz-Mellado a Carlos Rivera-Gómez a Carmen Galán-Marín a* a Departamento de Construcciones Arquitectónicas 1, Instituto Universitario de Arquitectura y Ciencias de la Construcción, Universidad de Sevilla, Avda. Reina Mercedes, 2, 41012, Seville, Spain b Department of Engineering Science, University of Oxford, Parks Road, Oxford OX1 3PJ, United Kingdom c Future of Cooling Programme, Oxford Martin School, University of Oxford, Oxford OX1 3BD, United Kingdom * Corresponding author. E-mail address: [email protected] 2 Abstract Extreme heat events are expected to occur more often as a consequence of climate change. This paper quantifies the impact of urban climate on building performance and evaluates the benefits of specific microclimates, such as inner courtyards, to mitigate extreme heat impacts. A reference case study associated with two outdoor weather conditions, an inner courtyard and a local urban climate, was measured, simulated and validated in TRNSYS. The validated model was then compared to three building models with a single outdoor weather condition associated with the urban climate, weather data from a rural station and a typical year weather file. The models were evaluated in free-running conditions and with air-conditioning systems. The results show how urban climate can increase indoor discomfort hours by 32% in free-running conditions and demonstrate that courtyard microclimate can almost completely mitigate the impact of urban overheating in buildings, eliminating severe indoor discomfort hours by more than 88%. Moreover, the increase in cooling energy demand due to urban climate was reduced by more than 15% in the case of having air-conditioning systems. The findings manifest the importance of accurate weather data for building simulation and demonstrate how multi-nodal outdoor conditions can enable additional strategies to mitigate climate risks, highlighting urban microclimates as a promising strategy to tackle extreme heat events in buildings and cities. Keywords: courtyard; microclimate; passive cooling; building simulation; local climate zone 3 Nomenclature and abbreviations ACH air change rate, h-1 CV-RMSE coefficient of variation of the root mean square error DH discomfort hours, % EBC Energy in Buildings and Communities EPBD European Energy Performance in Buildings Directive HR relative humidity IEA International Energy Agency LCZ local climate zone MBE mean bias error NMBE normalized mean bias error R2 coefficient of determination t time T temperature, ºC TY Typical year UHI urban heat island WD wind direction WS wind speed Greek letters 𝛳 air temperature, ºC Φ heat gains, W Subscript Ap Appliances Int Internal Li Lighting Oc Occupancy 4 1. Introduction Global warming is increasing the risks of extreme heat waves, which are expected to occur more often and last longer (IPCC, 2014). The impacts of these climate-related extremes include alteration and irreversible impacts for people and ecosystems, increasing human morbidity (e.g., dehydration, heat stroke and heat exhaustion) and mortality (WMO and WHO, 2015). On a high-emissions pathway, the worst scenario related to very extreme heat waves in Europe is projected to occur in the low-altitude river basins and Mediterranean coasts of southern Europe, with its numerous densely populated urban centres (EEA, 2019). Moreover, the effects of heat waves will worsen in larger cities due to the urban heat island (UHI) effect. This will increase cooling demand in a classic feedback loop (Hong et al., 2020), with extreme summer temperatures in cities, currently up to 5ºC higher than rural surroundings (IEA, 2018; Yenneti et al., 2020). Targets to climate-related hazards through resilience should be adapted to buildings in order to reduce and manage the climate risks and improve the comfort and wellbeing of citizens (United Nations, 2015). For decades, the attention of researchers has mostly focused on the energy efficiency of buildings, as existing buildings play an important role in energy consumption and carbon emissions. Computer technology and building energy simulation tools have been combined with methods to compare the cost-effectiveness of energy conservation measures, which has been studied and debated for years. However, current simulation procedures present several challenges when addressing future building needs under climate change and urban climate projections relating to weather data, metrics and urban microclimate, especially at the residential level (Bardhan et al., 2020). They are further detailed in the following paragraphs. Firstly, the importance of weather data selection in building performance evaluations has been studied recently in different studies comparing rural meteorological stations and local climate zones (LCZs) (Yang et al., 2020); typical year (TY) weather files based on historical data and extreme weather conditions (Cui et al., 2017; Jiao et al., 2020; Siu & Liao, 2020); or TY weather files and future local climate projections (Mata et al., 2019; Shen et 5 al., 2019). The results prove that although building design procedures based on typical meteorological years provide a good representation of the long-term building energy performance and that these comparisons are reliable (Seo et al., 2010), they cannot effectively reflect the variation range and the overheating impact of LCZs. Some recent research proposes the creation of extreme year weather files in order to include more than a single typical year of weather data in building simulations (Crawley et al., 2019; Nik & Arfvidsson, 2017). (Perera et al., 2018) even proposed a numerical model extending the building boundaries to include the urban context. The aim is to contribute to the impact assessment of climate change on buildings without neglecting the urban environment during extreme weather conditions, which will affect more frequently and intensely in the future (EEA, 2019). However, the evaluation of the potential impacts and implications of these extreme urban weather conditions on building performance is still required. Secondly, the metrics used in most of the current energy performance certificate procedures are based on energy and carbon indicators, and do not consider passive performance or thermal comfort indices. (Samuelson et al., 2020) evaluated the climate adaptation potential of buildings, highlighting how building regulations and incentive programs should look beyond energy as a sole performance metric of interest and consider passive survivability and thermal interactions with the urban climate in order to efficiently support climate-resilient building design. An example of alternative performance metrics was proposed by (Fiorentini et al., 2019), who defined an enthalpybased index to measure the viability of ventilative cooling solutions. Other studies used adaptive conform models in order to compare the effectiveness of passive measures (Bienvenido-Huertas et al., 2020). However, other studies show how the trade-off between single passive performance and energy savings requires an additional statistical effort for an optimal design (Chi et al., 2020). (Lizana et al., 2019) evaluated passive cooling alternatives under free-running and idealised cooling conditions through a parametric analysis. The results showed that passive solutions specifically designed to reduce energy demand cannot provide benefits in freerunning conditions through adaptive comfort model, even worsening building performance. (Serrano-Jiménez et 6 al., 2019) demonstrated how building performance differs according to the energy-related occupant behaviour, showing high imbalances in the results offered by official procedures for retrofitting criteria. These previous studies demonstrate the need for standardised comfort metrics in standard procedures to guarantee the mitigation of heat impact in buildings. Thirdly, the benefits of specific urban microclimate environments, such as inner courtyards, are not considered by current decision-support tools, which can mitigate the impact of extreme heat conditions in buildings (Rojas- Fernández et al., 2018). (Zamani et al., 2018) and (Rivera-Gómez et al., 2019) showed how inner courtyards are a powerful urban microclimate alternative for passive cooling. Courtyard microclimate has been used for thousands of years in different climates worldwide (Taleghani, 2014). Their benefits were previously quantified from different evaluation approaches. For example, (Natanian et al., 2019) and (Natanian & Auer, 2020) evaluated the energy and environmental performance of a set of urban configurations through a digital workflow using EnergyPlus, Radiance and ENVI-met simulation engines, showing that among the block typologies, the courtyard typology achieved the optimal combination among the environmental criteria tested. (Rivera-Gómez et al., 2019) evaluated the performance of 20 different courtyards in Spain, showing how some courtyards can decrease extreme heat temperature by up to 15ºC. (Diz‐Mellado et al., 2021) and (V.P. López-Cabeza et al., 2021) have provided different numerical approaches to model the courtyard microclimate. Moreover, different studies have demonstrated how courtyard microclimate performance can be improved through different techniques, such as the use of shading devices (Victoria Patricia López-Cabeza et al., 2018), variations of surface reflectance (Kristian Fabbri et al., 2020), the introduction of vegetation (Altunkasa & Uslu, 2020) or modification of aspect ratio (Lopez-Cabeza et al., 2020), among others. These specific microclimates can solve the problem of the urban overheating effect, mitigating peak daytime temperatures and promoting higher air changes rate at night (cross ventilation) to cool down the indoor environment (Zhou et al., 2020). 7 Despite the benefits described, courtyard performance is never considered in building performance by existing building energy simulation (BES) tools or white-box models (Li et al., 2021), such as EnergyPlus, eQuest, TRNSYS, ESP-r, etc. Even though a calibrated and relatively accurate baseline building model can be achieved through the collection of a relatively complex set of input data (Coakley et al., 2014), the integration of courtyards as a climate-resilient solution to mitigate heat impacts in buildings requires the application of multiple outdoor weather conditions, not available in existing procedures. Most of the microclimate studies related to inner courtyards and other urban microclimates are focused on CFD-based modelling (K. Fabbri & Costanzo, 2020; Forouzandeh, 2018; V. P. López-Cabeza et al., 2018), without considering the influence on building performance. Moreover, the currently available weather datasets used in building simulation software are mainly obtained from weather stations located in remote or rural areas (Chan, 2011). This means that the impact of the urban climate or specific microclimates on the thermal and energy performance of buildings cannot be effectively evaluated. There is a need to update the regulatory framework and procedures to support the decision-making process towards climate-resilient building design, implementing a holistic climate-resilient approach to achieve a sustainable climate change adaptation of cities. This research aims to evaluate the impact of different urban climates in building performance and quantify the benefits of specific urban microclimate configurations, such as the inner courtyards, for mitigating the extreme heat impact. A reference case study associated with two outdoor weather conditions, an inner courtyard and a local urban climate, was measured, simulated and validated in TRNSYS. The building performance was then compared with three building models with a single outdoor weather condition, related to the LCZ climate affected by urban overheating, weather data from a suburban station and a typical meteorological year weather file for the region. The building models are compared in free-running conditions through discomfort hours (%) using the adaptive comfort model and with air-conditioning systems through cooling energy demand (kWh/m2). There are mainly three research contributions in this work supporting the three research gaps previously mentioned: 8  The implications of weather data selection in building performance in a hot climate in Europe are highlighted by comparing different outdoor datasets based on TY weather files, rural weather data and urban climate affected by urban overheating.  The need for additional metrics in building performance certificate procedures to face future building needs under extreme heat events is evaluated. Different energy efficiency measures are compared through a parametric analysis during an extreme climate period, using building performance indicators in free-running and idealised cooling conditions.  Finally, the potential benefits of specific microclimates on building performance, such as inner courtyards, are experimentally and numerically evaluated and quantified to promote energy and climate-resilient targets in freerunning and idealised cooling conditions. These contributions provide novel criteria to correctly update future building codes and policies looking beyond energy efficiency targets towards a climate-resilient pathway. They support the consideration of more appropriate weather data, comfort metrics and urban microclimate strategies to improve building performance under extreme weather events. The paper is structured as follows. First, scenarios of urban climates and microclimates affecting building performance are introduced and characterised. Second, the methodology for the evaluation of building performance is detailed for each climate scenario. Finally, the results are presented and discussed, showing the impact of different weather datasets on building performance and the benefits of courtyard typology to mitigate urban overheating. 9 2. Urban climates and microclimates affecting building performance. The building performance in the same urban context under different climate situations is evaluated through four scenarios, illustrated in Fig. 1. These are listed from the most realistic situation (Scenario 1), which considers the urban climate and microclimate affecting the building, to the standardised building evaluation using a TY weather file (Scenario 4). Figure 1. Building modelling scenarios under different urban climates. Scenario 1 (S1) represents the building performance associated with two outdoor climate nodes: the urban climate or LCZ, affected by urban overheating, and the courtyard microclimate. Scenario 2 (S2) consists of the building associated with a single outdoor climate node for the LCZ. In this case, the potential benefits of courtyard microclimate to mitigate heat impacts are not considered. Scenario 3 (S3) shows the building linked to a rural or suburban weather condition, without the impact of urban overheating. These weather conditions represent the most widespread data sources for weather file generation, which are obtained from official meteorological networks located in rural or suburban areas and do not represent urban climate (Tsoka et al., 2018). In this case, the weather dataset is based on the period in which the case study was monitored, corresponding with a specific climatic situation of the study period. 16 The courtyard surfaces facing the courtyard microclimate node were numerically modelled as an equivalent resistance layer with a boundary condition linked to the courtyard temperature dataset at 15.0 m. Additionally, solar radiative and convective gains of zones in contact with the inner courtyard were previously obtained by simulating the zones as external, and then manually introduced as hourly heat flow input value per zone. This numerical procedure, used to model the impact of a second outdoor microclimate in the building simulation, was verified using the same external conditions in both weather nodes, obtaining an identical behaviour. This modelling approach was then calibrated and validated using measured data. The following building scenarios are based on the same calibrated building numerical model (see section 3.4), in which just weather datasets are modified as follows. Scenario 2 only considers the weather data obtained from the LCZ for both outdoor nodes, previously defined. The selected urban climate is classified as open midrise (LCZ-5) (Stewart & Oke, 2012) and is characterised by a skewed urban configuration that favours heat accumulation (Ratti et al., 2006; Xu et al., 2021). This LCZ is the most extended in the city. Scenario 3 was evaluated using weather data from the nearest official AEMET (National Meteorological Agency in Spain) weather station in Seville (WMO 083900), which is located in a rural/suburban area (Government of Spain, 2019). Scenario 4 assesses the building performance using the most extended TY weather file for the region for the same timeframe. The weather dataset was obtained from EnergyPlus database (EnergyPlus. Weather Data for simulations, n.d.) and consists of an IWEC1 data file for Seville developed by ASHRAE in 2001. The files were derived from up to 18 years (1982-1999 for most stations) of archived hourly weather data from the US National Climatic Data Center. These TY weather files are an artificial hourly weather year created with historical weather data reflecting long-term average conditions for a location (Seo et al., 2010). They have been widely used and are useful for predicting energy consumption that is closer to the long-term average. 17 All building scenarios are modelled with the courtyard shadings. Weather datasets for scenarios 1, 2 and 3 have the same solar radiation values obtained from the rural weather station. 3.4. Model calibration and validation The numerical model was calibrated through an iterative simulation process integrating TRNSYS with a Python script to reduce uncertainties in input parameters. After each simulation, the Python script compares the measured and simulated values, automatically calculating recommended statistical indices for model validation defined in ASHRAE Guideline 14-2014 (ASHRAE, 2014). S1 was used for model calibration and validation since it considers the most real building performance. Fig. 4 shows the iterative simulation process and the final performance of the validated model (Fig. 4c). Figure 4. Iterative simulation process for calibration and validation. a, Dwelling floor plan indicating measured rooms for model calibration and validation. b, TRNSYS visualisation of simulated temperature profiles. c, Python script to validate the model through an iterative simulation and calibration process. The temperature data of four indoor rooms in the dwelling were used to calibrate and validate the model. Measured rooms are indicated in Fig. 4a: bedroom1 (B1), bedroom2 (B2), bedroom3 (B3) and corridor (C). The 18 measured data were interactively compared with the simulated data obtained from TRNSYS (Fig. 4b) as illustrated in Fig. 4c. Moreover, statistical indices were automatically calculated for each simulation process (dashed red line in Fig. 4c). The standard statistical indices used for model validation were the Normalized Mean Bias Error (NMBE), the Coefficient of Variation of the Root Mean Square Error (CV-RMSE) and the Coefficient of determination (R2), whose results are shown in Table 2. The validated model fulfils the ASHRAE Guideline, which recommends that for good hourly reliability, the simulation model should have an NMBE lower than ±10% and CV-RMSE lower than 30%. Additionally, a minimum R2 value of 0.75 is recommended (ASHRAE Handbook - Fundamentals, 2017; Ruiz & Bandera, 2017). Table 2: Statistical indices for model validation following ASHRAE Guideline 14-2014. NMBE CV-RMSE R 2 Bedroom1 (B1) -2.0% (< ±10%) 3.9% (< 30%) 0.77 (> 0.75) Bedroom2 (B2) -0.5% (< ±10%) 2.0% (< 30%) 0.93 (> 0.75) Bedroom3 (B3) -1.5% (< ±10%) 3.0% (< 30%) 0.84 (> 0.75) Corridor (C) -1.2% (< ±10%) 3.5% (< 30%) 0.76 (> 0.75) 3.5. Building performance indicators The building performance in different urban climate and microclimate scenarios was evaluated through the following indicators: percentage of discomfort hours (DH, %) in free-running conditions and cooling energy demand (kWh/m2) using an idealised cooling system. The percentage of discomfort hours (DH, %) was evaluated simulating the building in free-running conditions (without air-conditioning systems) through the adaptive thermal comfort model defined in EN 16798-1:2019. This model defines the range of acceptable operative temperature levels depending on the outdoor temperature. The discomfort period was calculated by each comfort category (I, II and III). Indoor operative temperature per scenario was calculated as a weighted mean value for each room area. Running mean external temperature (ϴrm, ºC) was calculated according to Eq. 2. This free-running condition is commonly found in residential buildings 19 in the Mediterranean climate, where air-conditioning systems, predominantly located in some single rooms within the dwellings, are used sporadically when necessary (Domínguez-Amarillo et al., 2020; Lizana et al., 2016; Sendra et al., 2013). Ѳ rm ( ed ) = ( Ѳ 𝑒𝑑 − 1 + 0 . 8 · Ѳ 𝑒𝑑 − 2 + 0 . 6 · Ѳ 𝑒𝑑 − 3 + 0 . 5 · Ѳ 𝑒𝑑 − 4 + 0 . 4 · Ѳ 𝑒𝑑 − 5 + 0 . 3 · Ѳ 𝑒𝑑 − 6 + 0 . 2 · Ѳ 𝑒𝑑 − 7 ) / 3 . 8 (2) where: Ѳrm(ed): mean external temperature of focus day. Ѳed-n: daily mean outdoor air temperature for n-days prior to focus day. The cooling energy needs (kWh/m2) per scenario were evaluated considering an idealised cooling condition with two cooling set-point temperatures in whole indoor dwelling spaces, taking as reference the values recommended by the National Energy Performance Certificate (EPC) procedure (IDAE, 2009) and (EN 16798-1, 2019): 27°C at night and 25°C throughout the day. A sensitivity analysis considering different cooling energy consumption patterns was also developed. Three cooling-related occupant behaviour patterns were considered according to low, medium and high energy consumption profiles (Serrano-Jiménez et al., 2019). Low consumption is associated with an intermittent cooling pattern of 4 hours per day. Medium consumption is related to the standard EPC procedure profile (15 hours per day). A high consumption pattern means continuous cooling throughout the day (24 hours per day with cooling on). All the results are correlated to the conditioned floor area to facilitate comparison (kWh/m2) (Lizana et al., 2018). 4. Results and discussions The potential benefits of courtyard microclimate to mitigate extreme heat effects in buildings are analysed and discussed in three steps. Firstly, the measured weather datasets related to different urban climates and microclimates are compared. Secondly, the building performance is analysed under free-running conditions and with the operation of air-conditioning systems, comparing the percentage of discomfort hours and cooling energy needs for each scenario. Finally, a sensitivity analysis is used to assess the potential benefits of inner courtyards 20 as a climate-resilient building configuration, highlighting the most influential parameters to take advantage of these microclimates. 4.1. Evaluation of different urban climates and microclimates. The weather datasets collected from different urban climates and microclimates from 01st June 2020 to 31st August 2020 are compared in Fig. 5. This shows the full distribution of the temperature data per dataset, with three horizontal lines indicating the median, maximum and minimum values. The datasets are associated with the specific microclimate of the courtyard at 4.5 and 15.0 m above the courtyard floor, the urban climate in the measured LCZ, the rural conditions of the Seville weather station and the TY weather file. Figure 5. Violin plot of weather datasets. The area filled in represents the data distribution. The data comparison shows how the urban climate associated with the LCZ presents higher temperature values compared to rural weather and the TY weather file. Moreover, the thermal buffer effect of the courtyard microclimate is highlighted at 4.5 m and 15.0 m, decreasing high peak temperatures. 21 Fig. 5 illustrates the evolution of maximum and minimum daily temperatures, which introduces a better understanding of the behaviour of each weather dataset. Figure 6. The maximum and minimum daily temperature of different urban climates and microclimates throughout the summer season. Urban overheating increases maximum daytime temperatures by a mean value of 0.8ºC and above 1.5ºC for peak days. Moreover, minimum night temperatures are increased by a mean value of 2.5ºC during the measured period, with some peak overheating days above 6ºC. The data collected also show the thermal benefits of the inner courtyard microclimate in comparison with the LCZ. The courtyard was able to mitigate daily maximum temperature by a mean value of 2.9 ºC and 5.1ºC at 15.0 m and 4.5m, respectively. Moreover, during extreme heatwave periods, with maximum outdoor temperatures above 41.2ºC, the courtyard was able to reduce peak temperatures by up to 7.7ºC and 12.4ºC at 15.0 m and 4.5 m, respectively. The greater the heat impact, the higher the thermal benefits of the courtyard microclimate. 22 Additionally, it should also be noted how the TY weather file for this region shows lower temperatures during the entire summer period (Figs. 5 and 6). In fact, minimum temperatures are much lower than those measured in the rural area, not reflecting the data registered for this period (Fig. 6). 4.2. Impact of urban climates and microclimates on building performance The impact of different weather datasets on building performance was evaluated through two conditions. The calibrated building model simulated under free-running conditions per scenario (without any air-conditioning system) is analysed and discussed in section 4.2.1, evaluating the discomfort hours through the adaptive comfort model per scenario. Afterwards, the building model simulated with an idealised cooling condition per scenario is analysed and discussed in section 4.2.2, evaluating the cooling energy needs per scenario. 4.2.1. Building scenarios under free-running conditions The mean indoor air temperature per building scenario in free-running conditions is represented in Fig. 7. S2, related to urban climate in the specific LCZ, presents the highest indoor temperature distribution. In contrast, S1 and S3, associated with the courtyard microclimate and rural climate, show a very similar indoor temperature data distribution. This similar performance between S1 and S3 highlights the potential heat mitigation effect of courtyard microclimate at 15.0m, which results in a similar performance of the S1 building model to that simply considering the rural weather dataset without the urban overheating effect. Additionally, the results in S4, associated with the TY weather file and taken as a reference value, show the lowest maximum, minimum and median values in comparison with other scenarios. 23 Figure 7. Violin plot of indoor air temperature per scenario simulated. The area filled in represents the data distribution and the three horizontal lines indicate the median, maximum and minimum values. The impact of different urban climate and microclimate scenarios on building performance was evaluated from the point of view of comfort in Fig 8. This figure illustrates the indoor operative temperature oscillation (Top, ºC) in relation to running mean external temperature (ϴrm, ºC) for all simulated scenarios according to the European adaptive comfort model (EN 16798-1, 2019). 24 Figure 8. Adaptive comfort model of dwelling performance per individual scenario, based on EN 16798-1:2019. The results show how the indoor operative temperature values obtained in S1 (orange) are lower than those in S2 (red). These lower temperatures are associated with the benefit of courtyard microclimate in S1, which can mitigate extreme heat impact in the building analysed in S2. The results highlight how considering two outdoor weather conditions affecting the building has a considerable benefit in final building performance. S3 (yellow), relating to the building affected by rural weather data, provides even lower indoor operative temperature periods than S1. In this case, the calibrated building model was simulated using just one outdoor weather condition related to rural weather, neglecting the impact of urban overheating which considerably increases the comfort period. Finally, S4, associated with the building model simulated using the TY weather file, results in much lower indoor operative temperatures. These results show how TY weather files cannot effectively reflect the building performance under extreme heat climate events. These files are useful in predicting long-term energy-related performance, but would not be suitable for supporting a climate-resilient building design. Fig. 9 quantifies the differences from the point of view of comfort between the building scenarios in different outdoor climates and microclimates. This shows the percentage of discomfort hours per comfort category according to the European adaptive comfort model (EN 16798-1, 2019), involving the entire period measured 25 from June to August (Fig. 9a) and the extreme heat period alone (see Fig. 6) from 20th June to 10th August (Fig. 9b). Figure 9. Percentage of discomfort hours (DH) per comfort category according to the adaptive comfort model defined by EN 16798- 1:2019. a, Discomfort hours calculated throughout the entire measured period, from 01st June 2020 to 31st August 2020. b, Discomfort hours calculated throughout the extreme heat period measured from 20th June 2020 to 10th August 2020 (see Fig. 6). The results clearly demonstrate the thermal benefits of courtyard microclimate for mitigating heat impact on buildings. A comparison of S1 and S2 shows how the courtyard was able to reduce discomfort hours by 26% (from 58.3% to 43.2%) during the entire measured period (Fig. 9a) and by 14% (from 68.1% to 58.6%) throughout the extreme heat period (Fig. 9b). Moreover, severe discomfort hours, associated with discomfort hours higher than category III, were almost eliminated, with a reduction of up to 88% for both timeframes. Furthermore, the comparison of S1 and S3 demonstrates how the thermal response of the building involving the courtyard benefits is very similar to the building performance with rural weather data, without considering the urban overheating effects. This means that the courtyard was able to mitigate almost all the building overheating produced by the LCZ in scenario 2. Finally, S4 shows a percentage of DH of 16.2% and 24.3%, respectively, much lower than other scenarios. 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