An empirical study on the compound effects of extreme weather and UHIon building energy consumption under local climate

Due to climate change, extreme weather (EW) events like heatwaves and cold snaps are becoming more frequent, challenging urban buildings and energy systems. Urban heat island (UHI) effects—where city centers are significantly warmer than suburbs at night—further impact heating and cooling demands of urban buildings. However, there is still a lack of systematic empirical studies linking meteorological data to building energy use, especially regarding the compound effects of UHI and extreme weather on urban building energy consumption and load during winter and summer. To address future complex climate conditions, we propose using localized weather data (LWD) that fully accounts for both background EW and UHI effects. Driven by suburban meteorological observations and high-resolution land cover data, the data is generated using the UWG urban canopy model and the UMEP tool on the QGIS platform to capture realistic local weather conditions around buildings. It can be directly input into urban building energy model (UBEM) for the corresponding local climate zones to simulate building energy use. Our study shows that LWD better captures seasonal building energy use and the effects of external and internal factors. Compared to suburban weather station data, accuracy improves by 29.6% in summer and 36.6% in winter during the typical year, and by 35.1% and 30.1% during the extreme weather year, respectively. Local air temperature (Ta) has the greatest impact on actual energy use, followed by solar radiation (Rad)—especially during summer heatwaves, when Rad may exceed Ta in influence. Internal disturbances have a greater impact in summer, but their influence lessens during extreme weather due to stronger external climatic effects. This method supports refined assessment and control of UBEM across climates and seasons, helping manage energy peaks during heatwaves and prevent overheating in winter, ultimately aiding real-weather-based energy system optimization and urban design.