The Role of Geographic Scale of Weather Data in Urban Building Energy Models

Building energy consumption is sensitive to fluctuations in outdoor climate conditions, making weather data a crucial factor in determining the accuracy of building energy models. However, the influence of spatial scale on weather data and its effect on energy demand simulations has received limited attention. To address this gap, the present study applied an innovative approach integrating urban climate modeling and urban building energy modeling to evaluate the impact of meteorological data at varying spatial scales on buildings' energy consumption in the high-density urban environment of Seoul. This integration enabled an assessment of how weather data at different spatial scales, ranging from city-wide to individual grid levels, influenced buildings' energy use intensity. The results revealed that using generalized weather data, such as in the Typical Meteorological Year (TMY) files, underestimated energy demand. In contrast, applying localized weather data, particularly at the subdistrict level, led to a significant increase in monthly energy use intensity and predictive accuracy compared to the TMY case, which reached 56.46 % and 14.54 %, respectively. These results highlight the importance of considering finer-scale weather data in urban energy simulations to better capture the effects of local climate variations on cooling energy demand.