Assessing urban morphology's impact on solar potential of high-rise facades in Hong Kong using machine learning: An application for FIPV optimization

The relationship between solar potential on building façade and urban morphology at urban scale remains unclear, and the design of façade integrated photovoltaic (FIPV) lacks evidence. This study investigates high-rise, high-density commercial districts in Hong Kong (HK), using Random Forest algorithm combined with the SHapley Additive exPlanations method to assess the importance of urban morphology on solar irradiance on building facades. The results indicate that plot ratio, building floor, building density, and perimeter shape factor are the four key parameters influencing solar irradiance, with the importance rate and contribution value of the four parameters reach 43.9 % and 48.7 %, respectively. Based on these parameters and actual urban blocks in HK, typical urban typologies were constructed. Four scenarios were generated with plot ratio as the control parameter. The positions, amounts, and transparency of PV glass on the south and east facades were optimized to minimize the payback period and maximize power generation, using Non-dominated Sorting Genetic Algorithm II algorithm. The south façade of Scenario 1 (when the heights of surrounding buildings are lower than that of the targeted building) obtained the optimal payback period (8.44 years) and power generation (55961 kWh), with 77 opaque PV panels and 49 semi-transparent ones.