Analyzing the effect of land use patterns on cooling energy demand for residential buildings in Montreal: A Weather Research and Forecasting (WRF) model approach

doi:10.21203/rs.3.rs-3814552/v1

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Analyzing the effect of land use patterns on cooling energy demand for residential buildings in Montreal: A Weather Research and Forecasting (WRF) model approach

https://doi.org/10.21203/rs.3.rs-3814552/v1

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The relationship between Urban Heat Island (UHI) phenomenon and cooling energy use in buildings has been widely observed and studied. In this paper, the effect of land use (river, urbanization, and vegetation) is investigated to examine how vegetation and river can dampen the effect of the UHI in various locations and its influence on the cooling energy demand of Montreal residential buildings. The climatic data for the period of interest from May 15th to June 29th, 2020 covering two heatwave periods are simulated by Weather Research and Forecasting (WRF) modeling, and validated by data against weather observing stations (WOS). The results show that the average reduction in the cooling energy demand (CED) of the urbanization scenario compared to the river scenario is: 7% for the suburban location, 10.8% for locations facing the river, 1.2% for the rural location, and ranges from 3.4–7.6% for the urban locations, whereas compared to the vegetation scenario, the reduction in the CED is less compared to the river scenario except for locations facing the river. The daily average of CED during the heatwave period is almost twice as high compared to that of the remaining summer period. This study provides insights in the potential of cooling mitigation solutions.

Urban heat island

climatic data

Cooling demand

WRF

Cooling degree hours

Land use.

The mesoscale meteorological model WRF is used to generate local climatic data.
The WRF weather data are validated by weather station observation data.
Land use scenarios are introduced to mitigate the effect of UHI and heat waves.
Land use affects highly the cooling energy demand in residential buildings.
Applying CDH in changing climate scenarios to estimate the cooling energy demand.

Urban population, according to the United Nations, represents about 55% of world population and this proportion is expected to increase to 68% by 2050 (2018). In Canada in 2015, the building sector, accounting for residential, commercial and institutional buildings, was responsible for 28.3% of all energy used and 22.6% of greenhouse gas (GHG) emissions (NRCan 2016). Worldwide, 40% of building energy requirement is due to cooling and heating demands (IEA 2016). According to the Future of Cooling Report of the International Energy Agency (IEA), needs for space cooling will grow in the future worldwide, and the energy requirements for air conditioning are expected to triple by 2050 (IEA 2021). The energy consumption of buildings is not only influenced by the characteristics of the building envelope and the equipment (Santamouris et al. 2001; Ihara et al. 2008), but also by the environmental conditions. Because city-built areas have substantially different characteristics than natural surfaces, urban climates may differ significantly from original natural environment (Sun and Augenbroe 2014). The urban heat island (UHI) is characterized by higher temperatures in urban areas compared to surrounding rural areas (Santamouris 2007; Chapman et al. 2017), mainly due to heat storage in heavy constructions, lack of vegetation, reduced heat removal by ventilation and nighttime longwave radiation, anthropogenic impacts (particularly, waste heat released from heating, ventilation, and air-conditioning systems) (Magli et al. 2015). Numerous measures have been proposed to mitigate the negative impacts of UHI on the urban microclimate, particularly during heatwave periods (Jandaghian and Berardi 2020). One main strategy to alleviate urban thermal stress as well as to improve the human thermal comfort and decrease building energy demand, is to increase vegetation cover via green infrastructure such as forests, parks, and farmlands in urban areas (Wang et al. 2016; Kong et al. 2017; Arghavani et al. 2020). Several studies show that changes in ambient outdoor conditions caused by climate change (Pérez-Andreu et al. 2018; Ascione et al. 2021) and by UHI negatively impact the building energy demand.

Urban heat island effect and heatwave periods in cities have been studied for several types of climates. Building energy simulations (BES) (Eames et al. 2012; Fumo 2014) are influenced by weather data (climatic data), as shown e.g. in Toronto, demonstrating the effect of climate change on heating and cooling energy requirements for reference buildings (Berardi and Jafarpur 2020). In some cases, it is also shown that, in a cooling dominated climate (Ciancio et al. 2018; De Masi et al. 2021), not considering UHI leads to an underestimation of cooling energy consumption of 35–50%. The accurate prediction of the cooling energy demand (CED) of buildings during the summer period is not straightforward since input data for BES are numerous and uncertain and may change from year to year. For this reason, the Canadian National Energy Code uses the TMY algorithm (Typical Meteorological Year data) and software to develop Canadian Weather Year for Energy Calculation (CWEC) files for several locations in Canada (ASHRAE 2021). It is more and more accepted that weather data that reflect the local urban microclimate, heatwaves and potential future climate conditions has to be used for BES (Villa 2021). Mesoscale models are numerical meteorological models that have been used to simulate, analyze and predict urban weather conditions. Among the models that can be used, the Weather Research and Forecasting (WRF) model presents a high temporal resolution and flexible adaptability (Skamarock, W. C., Klemp, J. B., Dudhia, J., Gill, D. O., Liu, Z., Berner, J., … Huang 2019). The WRF model approach has shown promising results for studying the urban heat island effects (Bahi et al. 2020) and offers the possibility to overcome the difficulties and limitations of in situ measurement and assess potential future impacts (Kong et al. 2021). For example, WRF model was used to simulate waste heat release in cities at the mesoscale with high temporal resolution (Chen et al. 2014; Zhang et al. 2016). Krayenhoff et al. (Krayenhoff et al. 2021) conducted a systematic and critical review of the literature on numerical modeling of urban air temperature reduction resulting from green-blue infrastructure and reflective materials. When it comes to study the impact of vegetation in cities, several parametrization are available for WRF (Loughner et al. 2012). WRF with the urban canopy model (WRF-UCM) allowed estimating that covering urban streets with 50% tree coverage and reducing the quantity of asphalt on the roadway by 10% might lower daily urban canyon air temperatures by 4.1°C (Christopher et al., 2012) (Loughner et al. 2012).

Prior studies have estimated that increased urban albedo is an effective way of lowering ambient temperatures. Using also WRF with UCM and a dynamic downscaling approach, Vinayak et al. (2022) proposed and evaluated a mitigation strategy (urban albedo enhancement) to counter the unfavorable effects of future urbanization on the local climate over the Mumbai in India. Simulation results show that the average maximum surface air temperature of the Mumbai Metropolitan region would increase by 1.4°C due to future urbanization by 2050, whereas when adopting the proposed mitigation strategy, this increase would be restricted to 0.7°C (Vinayak et al. 2022).

Jandaghian & Akbari (2021) estimated in their study in Toronto and Montreal, Canada that an increase in the albedo could lead to 1–2°C reduction in ambient temperature (Jandaghian and Akbari 2021). More recently, Hosseini et al. (2022) in their study applied the degree-day method to assess building energy for the projected future climate changes (Hosseini et al. 2022). From WRF model results at a spatial resolution of 250 m, more local weather data can be determined and used to perform building energy simulations (BES) (Salata et al. 2022). BES are widely performed to predict the space heating and cooling energy demand of buildings (Allegrini et al. 2012; Zhou et al. 2020; Jafarpur and Berardi 2021).

As previously stated, there has been a significant amount of research devoted to the assessment of energy demand for buildings in a changing climate and/or taking into account the UHI. However, Canadian regulations as well as the majority of past research are highly based on a daily energy index (i.e. cooling degree-days) to correlate energy demand in scenarios' changing climate and UHI (Sun and Augenbroe 2014; De Rosa et al. 2014; De Masi et al. 2021; Hosseini et al. 2022). Although the CDD is an important index reflecting the climate and the energy demand of buildings, this index is based on the average daily temperature and does not take into account the variation in the hourly temperature. Contrary to that, applying cooling degree-hours (CDH) in changing climate scenarios, as an index linked to the hourly outdoor air temperature, can be represented as a term included in the computation of the cooling energy demand of a typical building. Indeed, this index makes it possible to evaluate the potential of natural ventilation and to analyze heat wave periods. This quantity index is proportional to the cooling energy demand.

The present study investigates the impact of land use of three scenarios (urbanization, river, and vegetation) to examine to which extent introducing large areas of vegetation and water (river) can dampen the effect of the urban heat island (UHI), especially during heatwave periods and their effect on the thermal comfort of building occupants and the cooling energy demand (latent cooling energy) of residential buildings in a large city. For this study, we select a large city surrounded by water, Montreal, a metropolitan region of Montreal, where the urban heat island becomes more and more prominent and in strong progression in certain of its districts (Cavayas and Baudouin 2008). The WRF ARW (Advanced Research and Weather Forecast) model is used to determine the local microclimate where the UHI phenomenon is taken into account. In addition, thanks to WRF's temporal and spatial high-resolution methods, cooling degree-hours are calculated taking into account land use properties covering two heat wave periods in the Montreal region.

We compare the actual scenario the river surrounding the island of Montreal to two fictitious (extreme) scenarios: in a first scenario the river is replaced by urbanization, and in a second by vegetation. Our investigation is conducted for late spring and early summer season, from May 15th to June 29th, 2020, covering two heatwaves and focusing on the effect of heatwaves and the urban heat island phenomenon at seven representative locations in Montreal. The three scenarios are compared in terms of indoor thermal comfort and building energy demand.

The present study was carried out over the metropolitan area of Montreal, Quebec, Canada. Montreal is located on an island in the Saint-Laurence River and surrounded by suburbs (Fig. 1). The island of Montreal is spread over 473 km² with a length of approximately 50 km long and a maximum width of 16 km, shoreline being 266 km long. Montreal climate is classified as a humid continental climate and has four distinct seasons with very cold and snowy winters, and summers, June to mid-September, that are warm to hot and humid.

To analyze the impact of urbanization, river, and vegetation on the thermal comfort of occupants and the sensible cooling energy demand residential buildings in Montreal, seven representative locations have been selected ranging from close to downtown districts to peripheral areas of the island (Fig. 1). The seven locations are: (i) St-Isidore representing a rural location in the south of the Montreal island; (ii) Ste-Anne, a suburban location to the West of Montreal, situated near a forest and receives dominant wind from the west bringing in rural air; (iii) Aqueduc, a location at the river border facing prevailing winds; (iv) LaSalle, an urban location near the river and facing prevailing winds; (v) International airport of Montreal, situated West of Montreal; (vi) McTavish, situated near Montreal downtown close to Mont-Royal Park, and (vii) Rosemont, an urban location in the center of the island. These locations are the same as in a previous study analysing the impact of local urban climate on indoor comfort and building energy demand (Boudali Errebai et al. 2022a).

3.1 Weather observations and WRF configurations

In terms of climate data, the data for three of the seven locations, i.e. McTavish, Airport and Ste-Anne are collected from weather observation stations in Montreal. For the other four locations, i.e. Rosemont, LaSalle, St-Isidore and Aqueduc, their climate data are obtained from WRF simulations (Skamarock, W. C., Klemp, J. B., Dudhia, J., Gill, D. O., Liu, Z., Berner, J., … Huang 2019), using the actual land use, i.e. representing the river as river. For the urbanization and vegetation scenarios, weathers data were obtained from WRF simulations for the seven studied locations by replacing the river by urbanization and vegetation (Table 1). All WRF simulations were based on the climatic data of the year 2020.

The WRF simulations are carried out over a period 45 days from May 15th until June 29th 2020. As boundary conditions, ERA5 the fifth generation ECMWF (European Centre for Medium-Range Weather Forecasts) reanalysis for the global climate and weather for the past 8 decades is used (ECMWF 2021).

The process of setting up the domain involves three levels of downnesting from ERA-5 data. The first level has a horizontal resolution of 6250 m and consists of 98 x 91 cells. The second level has a higher resolution of 1250 m and consists of 276 x 276 cells. The third and innermost level has the highest resolution of 250 m and consists of 351 x 351 cells. To accurately represent the main land types in the innermost domain, it is necessary to use static input data for land use with the same resolution of 250 m. To achieve this, a land use dataset from Environment and Climate Change Canada is converted into a WRF static dataset and mapped to MODIS land use categories (Latifovic et al. 2017). Since there is no steep terrain in the central domain, the standard resolution height dataset of 250 m is used. For soil types, no higher resolution data is available.

Table 1

Weather observations and WRF configurations for the three scenarios.
Locations	Weather observations (Real scenario)	WRF Model
Locations	Weather observations (Real scenario)	River scenario	Vegetation scenario	Urbanization scenario
McTavish	✓		✓	✓
Ste-Anne	✓		✓	✓
Airport	✓		✓	✓
Rosemont		✓	✓	✓
LaSalle		✓	✓	✓
St-Isidore		✓	✓	✓
Aqueduc		✓	✓	✓

The three scenarios of this study are as follows. First, the river scenario, which represents the actual situation, is implemented with the land use configuration ‘river’. Second, in the scenario urbanization all river cells with land use type water are replaced by an urban type, which means that the whole Saint Lawrence river is replaced by urban cells. This is an increase in urban cells of 25.7% in the greater Montreal area compared to the river scenario. Finally, in the scenario vegetation all river cells with land use water are replaced with the MODIS land use category “Mixed Forests”. The average value of the variable VEGFRA describing the share of vegetation in the respective cells and, is calculated for the whole inner domain and applied to the cells changed in the scenarios vegetation and urbanization.

The map of land use categories in and around Montreal is visualized in Fig. 2. The first plot (a) shows the scenario river with the island of Montreal surrounded by the Saint Lawrence river. The blue cells of the river area are defined as land use category 20 which corresponds to water bodies. The second plot (b) shows the scenario urbanization where the rive is replaced by land use of category 13 which corresponds to urban and built-up area (grey color). On the third plot (c), the scenario vegetation shows that all the cells of the Saint Lawrence river are replaced by land use category 5 which denotes mixed forest (orange color).

3.2 Building models description

A residential building with one floor, front façade facing South with area of 78 m², four exterior walls and a total window area of 16 m², which represent a windows fraction of 18%, is studied as shown in Fig. 3. Internal lighting gains of 7 W/m² and 4 people with standard equipment with established operating scenarios were used. The choice of this type of building was made after a previous study (Boudali Errebai et al. 2022b), where it was observed that residential individual buildings have the highest CED compared to residential collective buildings.

In this study, two thermal insulation levels are considered for walls, roof, and windows (Table 2). The first level (B_level_1) is representative for an insulated buildings during the 1950–1980 period. The second level (B_level_2) represents well insulated buildings built between 1980 to 2010. According to the 2020 edition statistics published by the city of Montreal [44], the building stock built before 1961 represents 45.3% of the total housing stock (22.2% before 1946 and 23.1% between 1946 to 1961), 30.1% between 1961 and 1980, and 19.3% between 1981 and 2010. The U-values of building components are calculated according to the ISO 6946 standard [45]. The solar heat gain coefficient (SHGC) of the glazing for normal incident solar radiation is 0.70 and 0.57 for insulation levels 1 and 2, respectively (Table 2). The U-value of the glazing of windows is 2.51 and 1.49 W/m2.K for insulation levels 1 and 2, respectively.

The setpoint temperature for cooling is chosen to be 22°C. In the case of simulations where the air conditioning system is used, the minimum air change rate (ACH) is 0.5 during the day, while, for the night ventilation, an ACH of 3 from 19:00 to 07:00 is turned on when the outdoor air temperature drops below 22°C (the chosen setpoint temperature). While in the case of simulations without air conditioning system, the air change rate (ACH) of natural ventilation is 3 ACH when the outdoor air temperature drops below 26°C. Inside blinds with a medium reflectivity slats of 0.5 are closed when the incident solar irradiance on the facade exceeds 120 W/m².

Table 2

Building envelope and glazing of windows properties.
Thermal insulation level	Building envelope U-Value W/(m²⋅K)		Glazing of windows		Buildings mean U-Value W/(m²⋅K)
Thermal insulation level	External walls	Roof	U-Value W/(m²⋅K)	SHGC	Buildings mean U-Value W/(m²⋅K)
B_level_1	0.47	0.30	2.51	0.70	0.58
B_level_2	0.28	0.18	1.49	0.57	0.36

3.3 Modeling methodology

Simulations are ran with transient 3D multizone software DesignBuilder V7 with graphical interface (DesignBuilder 2021) which uses the EnergyPlus 9.4 engine (2020). EnergyPlus is a BES program, widely used in various scientific research (Liu et al. 2015; Toparlar et al. 2018), regarded as one of the most detailed tools for calculating thermal comfort and building energy use of buildings in a transitory regime (Loonen et al. 2017), and confirmed by the US Department of Energy (DOE) (Deru et al. 2011), and the Pacific Northwest National Laboratory (PNNL) (Goel et al. 2014) to be accurate. EnergyPlus has been validated in different studies available in literature (Baniassadi et al. 2018; Ascione et al. 2021). In addition to conduction and convective heat transfer, EnergyPlus accounts for direct and diffuse solar radiation and handles all radiation exchanges between surrounding buildings, the ground, and the built environment as well as shadowing by surrounding buildings (Hadavi and Pasdarshahri 2021).

The considered period from 15th May to 29th June, 2020 represents an early summer period when first heatwaves begin to appear, in this case two heatwaves periods (usually defined as periods with high temperatures above the normal observed during several consecutive days (He et al. 2019), but the criteria for identifying and qualifying them vary widely). To generate modified EPW weather files, two open-source software tools have been used. The first is Elements to edit custom weather files for building energy modeling and the second is utility Weather of EnergyPlus to generate the new EPW weather files. In total 2×7×3 (42) cases are considered: two insulation levels and seven climatic data which represent different locations in Montreal and three scenarios (river, urbanization and vegetation).

3.4 Cooling Degree-hours method

Cooling degree-hours (CDH) is commonly used as a proxy variable to assess the energy demand for air conditioning buildings (Salata et al. 2022). CDH measures how many degrees Celsius (°C), and for how long (h), the outside air temperature is higher than a base temperature (Li 2016). As air conditioning demand depends on outdoor air temperature but also on solar gains, internal heat gains and outdoor humidity, CDH provides only a qualitative estimation of the energy demand according to ISO15927-6:2008 standards (15927-6 2007). However, since in this study only the outside air temperature is varied for different locations in Montreal while other climate data are kept constant (relative humidity, wind speed, atmospheric pressure, solar radiation, etc.), CDH may provide a good measure to qualitatively compare the cooling energy demand at different locations. The use of CDH to determine the cooling energy demand can provide a first fast indication of the amount of energy required to cool a building based on the region where it is located and local weather conditions without the need for more advanced simulations requiring significant input data, thereby saving time and resources.

CDH can be used for comparison and refers to the extent of cooling demand in degree hours (°C h) for a chosen temperature threshold (base temperature). CDH is defined as the cumulative sum of the temperature difference between the hourly outside reference and base temperature.

Using the outside temperature at certain locations, the local CDH can be compared for assessing the local cooling energy demand at these specific locations (Thambidurai et al. 2015). The choice of base temperature is important and differs in several studies. In (ASHRAE 2013), CDHs are given for base temperatures of 23.3°C and 26.7°C (upper limit for human comfort) according to ASHRAE Standard 55. In this study, the cooling degree hours are determined for a base temperature of 22°C (CDH₂₂) which corresponds to the same temperature selected for the cooling setpoint.

4.1 Validation

Validation of the WRF simulations is done by comparing, the simulated hourly temperature at 2 m with public available data from weather stations in the Montreal area. These are the same stations for which measured climate data are used as input for the building energy calculations. For validation we calculate the RMSE (Root mean square error), R² (Coefficient of determination), ME (Mean error, Model Bias) and MAE (Mean absolute error). The results of the validation are listed in Table 3.

The RMSE results show similar accuracy for all stations and are close to another recent study with mesoscale data on Montreal heat islands (Teufel et al. 2021). Further investigation of the results shows a lower RMSE during the day than during nighttime. R² results show a very good agreement between model and results. The ME values indicate a general overprediction of the temperatures during the simulation period. Comparing the MAE and the RMSE values show outliers are not dominating the simulation performance.

We note that the location of the current stations is not optimal to represent the urban climate in Montreal. They are close to a hill with trees (McTavish), at the edge of a small urban center (Ste-Anne) or close to the river situated on a large open space (Airport).

Table 3

Scalar accuracy measures of 2 m temperatures at the weather observation stations of the WRF model from May 15th to June 29th 2020.
Weather Station	RMSE [°C]	R²	ME [°C]	MAE [°C]
Ste-Anne	2.68	0.93	1.44	2.07
Airport	2.91	0.94	2.09	2.37
McTavish	2.60	0.94	1.73	2.06

4.2 Analysis of climatic data

Figure 4 compares the hourly outdoor air temperature at three locations with St-Isidore representing a rural location, Aqueduc a river border location, and Rosemont an urban location during the period from May 15th to June 29th. During the 11 days of heatwave period (from May 26th to May 29th and June 17th to June 23th ), the average temperature in the Rosemont location is higher than that of the St-Isidore location by 1.3°C during the day and 4.3°C during the night, while it is 0.2°C higher during the day and 1.1°C during the night compared to the Aqueduc location.

To analyze the outdoor air temperature for all locations for the three climate scenarios, Fig. 5 shows the percentage of occurrence in three temperature ranges: below 18°C representing the cooler conditions, 18°C ≤ T ≤ 30°C representing an acceptable range of outside temperature, while the range with temperature over 30°C represents heatwave conditions. For the mentioned period of late spring and early summer in 2020, temperatures above 30°C occur more frequently in urban locations (Aqueduc, LaSalle, McTavish and Rosemont) compared to rural locations (St. Isodore). On average, around 9.4% of the time heatwave conditions occur, ranging from 7.1–11.2% for different locations and scenarios, with a maximum for the urbanization scenario followed by vegetation scenario while the river scenario presents the lowest values. In contrast, for cooler conditions (T < 18°C), the highest values are observed for the case of the river scenario followed by the vegetation scenario, while the urbanization scenario represents the lowest values with an exception for the Aqueduc location where the highest occurrence time is observed for the case of the vegetation scenario followed by the river and urbanization scenarios.

There are significant temperature differences between the urbanization and river scenarios as well as between the urbanization and vegetation scenarios especially during heatwave periods. Figure 6 shows the reduction in mean daily temperature (∆T_d) comparing urbanization to river (vegetation) scenario for the heatwave periods (from May 26th to 29th and June 17th to 23th ) and the remaining non-heatwave periods (from May 15th to 25th, May 30th to June 16th and June 24th to 29th ). The mean daily temperature reduction urbanization – river (Fig. 6a) is rather small for the locations St-Isidore, Airport, McTavish and Rosemont locations (less than 0.5°C), while in the urbanization - vegetation scenario the temperature reduction is even smaller and is less than 0.2°C. For Ste-Anne location, the mean daily temperature differences are 0.4 and 0.9°C, respectively for urbanization-vegetation and urbanization-river scenario. The most significant temperature reduction is observed for Aqueduc and LaSalle with a ∆T_d of 1.2°C and 0.5°C respectively for the vegetation scenario and 1.5°C and 1.3°C for the river scenario, resepctively. Compared to the non-heatwave periods shown in Fig. 6b, the mean daily temperature reduction is significantly smaller than those found for the heatwave periods.

In summary, the river scenario shows a more significant temperature reduction than the vegetation scenario compared to the urbanization scenario. This may be explained by cooler winds flowing from a smooth water area, compared to a vegetated area with higher roughness and less cooling potential. This shows the beneficial influence of river scenario to reducing the air temperature and the urban heat island effect.

Figure 7a shows the CDH for the seven locations and the three scenarios, together with the reduction in CDH relative to the urbanization scenario (Fig. 7b). For the urbanization scenario, the CDH is higher for urban locations (Aqueduc, LaSalle, McTavish and Rosemont) than for suburban (Ste-Anne) and rural (St-Isidore) locations. Using the urbanization scenario as reference, the river scenario shows in general a higher cooling potential than the vegetation scenario. The highest reduction in CDH is observed for Aqueduc located close to the river, while the other locations show a smaller reduction since located further from the river.

4.3 Analysis of indoor thermal comfort

During the late spring and early summer period, HVAC systems are generally turned off, with a probable exception during heatwave periods. We assume in this analysis the air-conditioning system not to be in operation Fig. 8 shows the cumulative distribution of hourly operative temperatures (T_op) for Aqueduc location, the location with the highest cooling potential (see Fig. 7). The indoor thermal comfort zone, with operative temperature from 20°C to 27°C is highlighted in grey according to standard ASHRAE-55 2017 (ASHRAE Standard 55 2017). For the urbanization scenario (see Fig. 8a) the operative temperature is around 47% of the time in the comfort zone for the lower level of thermal insulation. For the vegetation and river scenarios, this duration amounts to 52% and 53% of the time respectively. Overheating conditions above 27°C occur around 50% of the time for the urbanization scenario, around 42% for vegetation scenario and around 43% for river scenario. Figure 8b shows the cumulative distribution of hourly operative temperature for the higher level of thermal insulation. The period of time of thermal comfort is around 48%, 55% and 54% respectively for urbanization, vegetation and river scenarios, while the period of time of overheating for these three scenarios is between 41% and 49%. It is clear that the period of time of indoor thermal discomfort in not air-conditioned buildings is much longer in the urbanization scenario compared to the river or vegetation scenario, while the insulation level has less influence on the duration of discomfort.

4.4 Cooling energy simulation

This section reports building energy simulation results for all considered scenarios and cases. Although in residential buildings, air-conditioning systems are usually activated only during periods of overheating and during heatwaves, use of air conditioning could be increasingly used throughout the summer period to improve indoor thermal comfort. Figure 9 shows the cooling energy demand (CED) for the period from May 15th to June 29th, 2020. For the buildings with a low level of insulation and a high level of insulation (Figs. 9a-9b), the CED for the urbanization scenario, river scenario and vegetation scenario for urban locations, rural location and suburban location are summarized in Table 4.

Table 4

CED in kWh/m² for: a) low level of thermal insulation and b) high level of thermal insulation.
Thermal insulation	Urbanization scenario			River scenario			Vegetation scenario
Thermal insulation	Urban locations	Rural location	Suburban location	Urban locations	Rural location	Suburban location	Urban locations	Rural location	Suburban location
Low level	20.3 to 21	16.8	17.9	18.6 to 19.8	16.6	16.7	18.4 to 20.3	16.6	17.1
High level	18.4 to 18.8	15.2	15.2	16.9 to 17.9	15	15.1	16.6 to 18.4	15.1	15.5

In terms of CED per day (Table 5), the CED is from 0.36 kWh/m²/day to 0.46 kWh/m²/day for the building with the lower level of insulation and from 0.33 kWh/m²/day to 0.40 kWh/m²/day for the building with the higher level of insulation.

The average reduction in the CED from urbanization scenario to the river scenario is 7% for the suburban location, 10.8% for Aqueduc (facing the river), 1.2% for the rural location, and from 3.4–7.6% for the urban locations. The average reduction in the CED for the vegetation scenario is 4.3% for the suburban location, 11.5% for Aqueduc, 0.8% for the rural location, and from 1.2–3.7% for the urban locations. A particular behavior is observed for the locality of Aqueduc, where the relative reduction in cooling energy demand in the case of urbanization-vegetation is higher than that of urbanization-river, which is opposite to the observation for the other localities. This particular behavior can be explained by Fig. 5 where it is noted that vegetation scenario has the highest percentage in low temperature (T < 18°C) compared to the other localities, which allows to benefit from natural night ventilation to reduce the CED. Since this locality is close to the vegetation area, shadowing and transpirative cooling of the vegetation will cool this area compared to the river case, where cooling is more limited to evaporative cooling effects.

For the building with the higher level of insulation (Fig. 9b), the same observations are noted with a reduction in the CED on average of 9.4% compared to the building with the lower level of insulation.

Table 5

Cooling energy demand per day, average over seven locations.
	Cooling energy demand per day [kWh/m²/day]
	All studied period	Heatwave period	Remaining period
B_Level_1	0.36–0.46	0.64–0.79	0.27–0.35
B_Level_2	0.33–0.40	0.57–0.70	0.25–0.32

To analyze in detail the CED during heatwave period separately from non-heatwave periods, Fig. 10 shows the cooling energy demand (CED) during the two heatwave periods and during the remaining non-heatwave periods. We remind that the heatwave periods range from May 26th to May 29th and June 17th to June 23th, which represent 24% of the considered time, while the non-heatwave periods range from May 15th to 25th, May 30th to June 16th and June 24th to 29th, which represent 76% of the considered time. Based on Fig. 10a, the CED during the heatwave periods represents 40–43% of the total CED during the considered period. The CED for heatwave periods ranges from 0.64 kWh/m²/day to 0.79 kWh/m²/day for the building with the lower level of thermal insulation and from 0.57 kWh/m²/day to 0.7 kWh/m²/day for the building with the higher level of insulation, which represents a high cooling demand per day compared the other periods (Table 3). In the non-heatwave periods (Fig. 10b), the CED covers 57–60% of the total CED during the considered period. However, the cooling demand per day is low and ranges from 0.27 kWh/m²/day to 0.35 kWh/m²/day for the building with the lower level of insulation and from 0.25 kWh/m²/day to 0.32 kWh/m²/day for the building with the higher level of insulation.

The results of Fig. 9 and Fig. 10 suggest that CDH can be used as a location-dependent metric for estimating the spatial distribution of cooling energy demand of buildings over a city taking into account differences in local climate over the city. It is observed that rural locations show a much lower cooling energy demand, mainly due to lower nighttime outdoor air temperature compared to urban locations showing a much higher cooling energy demand. In this perspective, the cooling degree hour metric CDH₂₂ is related with CED in Fig. 11, showing a linear relation for all scenarios and locations with a R² higher than 0.97. The root mean square error (RMSE) and mean absolute error (MAE) are also determined. The results are summarized in Table 4. The RMSE are 0.26, 0.25 kWh/m² and the MAE are 0.19, 0.18 kWh/m² for lower and higher building insulation levels, respectively. From the linear regressions, the average relative deviations for the low and high insulation levels are 1% and 1.1% respectively with maximum relative deviations of 4% and 4.1% respectively. This means that CDH can be used as an indicator to determine the cooling energy demand distribution over cities.

Table 6

R², RMSE, MAE and slopes of the simulated cases.
Insulations	R² [-]	RMSE [kWh/m²]	MAE [kWh/m²]	Slope × 10^− 3 [kWh/m².°C]
B_Level_1	0.97	0.26	0.19	5.0
B_Level_2	0.97	0.25	0.18	4.4

The choice of the reference (base) temperature for the calculation of the CDH has an important influence on linear correlation of CDH with CED as shown in Fig. 12. The level of linear correlation evaluated by means of R² coefficient is found to highly depend on the chosen reference (base) temperature and CDH correlates well with CED: with R² > 87% for higher reference temperature (at base temperature of 30°C), showing that CED gives fairly reliable estimates of CED for the different studied locations and scenarios. This shows that CDH can be considered as a first estimate for the cooling energy demand (CED). However, deviations between CED and CDH are observed, which can be attributed to natural night ventilation activated when the outdoor temperature is below the chosen setpoint temperature of 22°C, due to incoming solar radiation, conductive and convective gains/losses, heat storage in internal walls and floors, internal heat gains, which are not directly considered in CDH. Figure 11 it is clear that the curves between CDH and CED show different slopes for the different insulation levels. The slope can be seen as a measure for the sensitivity of the cooling energy demand to changes in outdoor temperature as measured by the CDH. The lower building insulation level curve shows a higher slope indicating a higher sensitivity to outside weather conditions, while the higher building insulation level shows less sensitivity to outside weather conditions due to the better insulation of the walls.

In the present study, we examined the effects of different land use scenarios on the cooling energy demand of residential buildings for different locations in Montreal. We replace the river (existing situation) by urbanisation land use or vegetation and examine how vegetation and river dampen the effect of the urban heat island (UHI) in the urbanisation case. The building cooling energy demand is evaluated at different locations in the city for these three land use scenarios representing river, urbanization, and vegetation and for two levels of insulation buildings. For the cooling energy demand assessment, outdoor temperatures are provided by climate data from weather station observations for 3 locations (Ste-Anne, Airport and McTavish) for the river scenario (real situation). For the other locations (St-Isidore, Aqueduc, LaSalle, and Rosemont) climate data predicted by Weather Research and Forecasting (WRF) modeling are used for all scenarios (river, urbanization and vegetation). The climatic data determined by WRF modeling are validated by weather station observation data and a reasonable agreement has been observed.

In this study, the period of late spring and early summer season from May 15th to June 29th, 2020 covering two heatwave periods has been analysed.

The approach of using the cooling degree hours (CDH) is used to determine polynomial relationships that link the CED of buildings and the outdoor temperature of each location. The CDHs are determined for each location and scenario showing an important variation of CDH over the city for different scenarios. Urban locations further away from the river show a lower potential for cooling mitigation strategies as compared to suburban and rural locations. Taking the urbanization scenario as a reference, the river scenario has in general a high potential for cooling mitigation compared to the vegetation scenario.

The thermal comfort inside buildings, assessed by the operative temperature, for the vegetation and river climate scenarios is almost the same.

The average reduction in the cooling energy demand (CED) of the urbanization scenario compared to the river scenario is 7% for Ste-Anne (suburban location), 10.8% for Aqueduc (location facing the river), 1.2% for St-Isidore (the rural location), and from 3.4–7.6% for the urban locations (Aqueduc, LaSalle, McTavish and Rosemont). The average reduction in CED for the vegetation scenario is 4.3% for Ste-Anne, 11.5% for Aqueduc, 0.8% for St-Isidore, and from 1.2–3.7% for the urban locations (Aqueduc, LaSalle, McTavish and Rosemont). The daily average CED during the heatwave period is almost twice as large as that during non-heatwave days.

ACH	Air Change Rate	MAE	Mean Absolute Error
BES	Building Energy Simulation	RMSE	Root Mean Squared Error
CDH	Cooling Degree Hours	UHI	Urban Heat Island
CED	Cooling Energy Demand	WOS	Weather Observation Stations
EPW	EnergyPlus Weather	WRF	Weather Research and Forecasting
LU	Land Use

Acknowledgement

The support of the Canada Research Chair Program and NSERC Discovery grant is acknowledged. We also acknowledge the support of Swiss National Science Foundation (SNF) project no. 169323.

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Title of Manuscript: Analyzing the effect of land use patterns on cooling energy demand for residential buildings in Montreal: A Weather Research and Forecasting (WRF) model approach.

Author(s): Farid BOUDALI ERREBAI, Dominik STREBEL, Jan CARMELIET, Dominique DEROME

We hereby grant consent for the publication of the paper titled ' Analyzing the effect of land use patterns on cooling energy demand for residential buildings in Montreal: A Weather Research and Forecasting (WRF) model approach' and acknowledge that the details, graphics, or images in this paper will be freely available on the Internet and may be accessed by the general public. We understand that the final decision on whether consent to publish is required lies with the Editor of Climatic Change Journal.

Competing Interests

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

Author Contributions

All authors contributed to this investigation paper, and each author read and approved the final manuscript. The specific contributions of each author are as follows:

Pr. Jan CARMELIET and Pr. Dominique DEROME:

Conceptualization: Developed the initial research idea and simulation framework.

Methodology: Designed the simulation protocol and defined the parameters.

Validation: Provided comments on the simulation results.

Manuscript Review: Offered insights and comments on previous versions of the manuscript.

Dr. Farid BOUDALI ERREBAI and Dominik STREBEL, Ph.D.:

Software: Implemented the simulation code and conducted simulations.

Results: Conducted data analysis and interpreted the results.

Writing: Contributed to the redaction of the first draft of the manuscript.

Funding

The list of funding source are:

Canada Excellence Research Chairs, Government of Canada.
NSERC Discovery Grant.
Swiss National Science Foundation (SNF) “169323”.

Availability of data and materials

All authors declare that the datasets used and analysed during the current study will be available on reasonable request.

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Analyzing the effect of land use patterns on cooling energy demand for residential buildings in Montreal: A Weather Research and Forecasting (WRF) model approach

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Abstract

Figures

HIGHLIGHTS

1 Introduction

2 Study area

3 Materials and methods

3.1 Weather observations and WRF configurations

3.2 Building models description

3.3 Modeling methodology

3.4 Cooling Degree-hours method

4 Results and discussion

4.1 Validation

4.2 Analysis of climatic data

4.3 Analysis of indoor thermal comfort

4.4 Cooling energy simulation

5 Conclusions

NOMENCLATURE

Declarations

References

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