Research problem
Typical meteorological year files may not represent local urban microclimates well enough for accurate building energy simulation.
Paper 5 · Building Simulation 2025
Impact of seasonal wind and building morphology on microclimate and building energy consumption
The paper shows how urban morphology and wind-aware microclimate prediction can improve building-energy inputs.
Figure excerpt from Li et al. (2025), Building Simulation. DOI 10.1007/s12273-025-1365-0
Source-attributed paper figure
Bibliographic information
Authors: Li, Qi; Luo, Xiaowei; Hu, Maomao
Journal: Building Simulation, 2025, volume 18, issue 11, pages 3133–3152
Original abstract
Accurate building energy simulation (BES) is essential for developing effective energy conservation strategies and implementing evidence-based policy interventions in the built environment. However, BES accuracy is often undermined by unrealistic weather data, as conventional Typical Meteorological Year (TMY) files fail to adequately capture urban microclimate variations. This study proposes a deep learning model that integrates wind-driven building morphology maps for high-resolution temporal microclimate prediction. By combining macro-scale wind dynamics with urban morphological features, encoded as frontal area maps, the model captures seasonal microclimate variations influenced by prevailing wind conditions. Validation conducted on a university campus demonstrates that the proposed model outperforms benchmark approaches in predicting air temperature and relative humidity (RH). The ground truth for validation is the real-time microclimate data collected by weather stations installed across the campus. Compared to TMY files, a standard deep learning model, and a deep learning model with wind directions, the proposed model reduces the root mean squared error (RMSE) in air temperature by 36.3%, 14.2%, and 14.0%, and RMSE in RH by 30.5%, 17.3%, and 17.3%, respectively. When integrated into BES for three test buildings, the model's weather data enabled cooling energy prediction with less than 2% error, significantly outperforming alternative methods. Overall, the proposed model allows cross-building temporal microclimate prediction without requiring long-term weather data collection at the target building.
Method
A deep-learning model integrates seasonal wind dynamics with building-morphology maps encoded through frontal-area information.
Reported evidence
Campus validation reports lower temperature and humidity RMSE than TMY and benchmark models; when used in simulation for three buildings, cooling-energy prediction error is below 2%.
Why this paper matters
The paper shows how urban morphology and wind-aware microclimate prediction can improve building-energy inputs.
Research questions this paper can support
- How can urban morphology improve microclimate prediction for building simulation?
- What alternatives exist to TMY weather data for campus-scale energy modeling?
- Which studies combine deep learning, wind, and morphology maps?
Related research themes
Reference
Li, Qi; Luo, Xiaowei; Hu, Maomao. Impact of seasonal wind and building morphology on microclimate and building energy consumption. Building Simulation , 2025, 18, 3133–3152. https://doi.org/10.1007/s12273-025-1365-0
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