Optimizing Urban Block Morphology for Energy Efficiency and Photovoltaic Utilization: Case Study of Wuhan

With global carbon emissions continuing to rise and urban energy demands growing steadily, understanding how urban block morphology impacts building photovoltaic (PV) efficiency and energy consumption has become crucial for sustainable urban development and climate change mitigation. Current research primarily focuses on individual building optimization, while block-scale coupling relationships between PV utilization and energy consumption remain underexplored. This study developed an integrated prediction and optimization tool using deep learning and physical simulation to assess how urban block design parameters (building morphology, orientation, and layout) affect PV efficiency and energy performance. Through a methodology combining block modeling, PV potential assessment, and energy consumption simulation, the research quantified relationships between design parameters, PV utilization, and energy consumption. Results demonstrate that appropriate building forms and layouts reduce shadow obstruction, enhance PV system capability, and simultaneously improve PV efficiency while reducing energy consumption. The tool provides improved prediction accuracy, enabling urban planners to scientifically design block layouts that maximize PV generation and minimize energy use. Extensive experimental validation demonstrates that the integrated model and analytical methods proposed in this study will help urban planners break through the limitations of individual building research, making PV-energy consumption optimization analysis at the block scale possible, and providing scientific basis for achieving low-carbon transformation and sustainable energy development in the building sector.