Optimizing building integrated photovoltaic facades: A parametric approach for enhanced energy efficiency and daylighting performance

This study proposes a parametric optimization framework specifically designed to enhance the performance of Building Integrated Photovoltaic (BIPV) façades. The aim is to identify optimal façade configurations that maximize both photovoltaic energy generation and indoor daylighting quality through multi-objective evolutionary algorithms. Using Grasshopper as the parametric modeling platform, the methodology integrates Ladybug and Honeybee for environmental simulations and Octopus for genetic algorithm-based optimization. The framework evaluates a range of window-to-wall ratio (WWR) scenarios to balance conflicting objectives: increasing solar on opaque façade areas for improved PV efficiency, and maximizing the indoor area exposed to daylight within comfort thresholds (300–500 lx). An algorithm was developed to generate diverse design scenarios, and Pareto front solutions were identified based on these dual criteria. Energy load simulations were conducted using OpenStudio to assess overall building performance. Compared to the baseline building profile, the optimized configuration resulted in a 26 % increase in solar irradiation and a 70 % improvement in daylighting coverage. When evaluated individually, the best irradiation-focused solution showed a 28 % gain, while the top daylighting-optimized scenario yielded a 9 % improvement. These findings demonstrate the value of integrating evolutionary optimization with parametric design tools to support early-stage, performance-driven BIPV façade design. The proposed methodology offers a replicable, scalable approach for architects and engineers aiming to develop energy-efficient, daylight-responsive building envelopes.