Assessing built microclimate with building group frontal projection maps: A sun-path-dependent deep transfer learning neural network approach

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Climate conditions play a pivotal role in estimating buildings' energy consumption. For urban-scale building energy dynamic simulation, the typical meteorological year weather represents a universal climate condition for the entire city. However, the varying terrain roughness, environment conditions, human-built architectures jointly formulate a unique microclimate condition for each building. This study intends to develop flexible and reliable approach to assess a building's microclimate condition based on its surrounding environment morphological features. The proposed approach leverages deep transfer learning neural networks that combines seasonal sun path trajectories and front projection maps of building groups. To validate the proposed method, a case study was conducted on a campus environment with wireless environmental sensing systems. The findings of this study demonstrate that the projection matrices and sun path can improve the assessment methods solely based on the dynamic impacts of urban morphological features and can forecast microclimate dynamics for urban-scale building energy simulation.