Urban microclimates, driven by urban form and regional climate, strongly affect building thermal performance and energy demand. However, many studies focus on individual cities or specific climatic zones, neglecting broader spatial heterogeneity and the combined effects of meso- and micro-scale climatic factors. To address this gap, this study integrates Grasshopper-based energy simulation, the Local Climate Zone (LCZ) classification, and 103 in-situ meteorological observations, including air temperature, relative humidity, wind speed and direction, and precipitation, to evaluate the differences in building heating and cooling demands between the LCZ-specific in-situ observation and those derived from the Typical Meteorological Year (TMY) data across five major climatic regions in China. Results show that built-type LCZs generally exhibit higher temperatures and lower humidity than the TMY baselines, resulting in reduced heating demand and increased cooling demand. Annual energy deviations range from −47.83% to +57.69%, depending on the climate region and LCZ type. The TMY data significantly underestimate peak heating loads, particularly in the heating summer and cold winter regions, while often overestimating peak cooling loads, with a maximum deviation of 42.03%. Monthly and hourly analyses reveal notable energy discrepancies during transitional seasons and diurnal extremes. Air temperature is identified as the primary factor affecting energy demand, with humidity and wind contributing secondary effects via correlation analysis. By coupling LCZ-based microclimate inputs with building simulation, this study establishes a transferable framework for more reliable prediction of heating and cooling loads. The approach enhances thermal management, supports energy-efficient system design, and contributes to sustainable, climate-responsive urban development.
Jia et al. (Fri,) studied this question.
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