Parametric Multi-Objective Optimization of Building Integrated Photovoltaic Façades
Building Integrated Photovoltaic (BIPV) façades play a critical role in advancing net-zero and energy-positive building strategies by simultaneously serving as envelope elements and renewable energy generators. However, optimizing BIPV façades is challenging due to competing performance objectives, particularly photovoltaic energy generation and indoor daylighting quality. This study proposes a parametric optimization framework to systematically address these trade-offs during early-stage design.
Challenges in Balancing Energy Generation and Daylighting
Façade design decisions, such as window-to-wall ratio (WWR), directly influence solar exposure on opaque surfaces for photovoltaic efficiency while also affecting indoor daylight availability and visual comfort. Increasing PV-active areas often reduces daylight penetration, whereas excessive glazing can compromise energy generation potential. These conflicting requirements necessitate a multi-objective optimization approach.
Parametric Modeling and Simulation Framework
The proposed framework is implemented using Grasshopper as the parametric design environment. Ladybug and Honeybee plugins are employed for solar radiation and daylighting simulations, enabling climate-responsive performance evaluation. Genetic algorithm-based optimization is conducted using Octopus, allowing automated exploration of a wide design solution space.
Optimization Strategy and Design Variables
An algorithm was developed to generate diverse façade configurations by varying WWR values. Multi-objective evolutionary optimization was applied to simultaneously maximize solar irradiation on opaque façade areas and increase the proportion of indoor spaces achieving acceptable daylight levels (300–500 lx). Pareto front solutions were identified to represent optimal trade-offs between these objectives.
Energy Performance Evaluation and Results
Building energy simulations were conducted using OpenStudio to assess the overall impact of optimized façade configurations. Compared to the baseline building model, the selected optimal solution achieved a 26% increase in solar irradiation and a 70% improvement in daylighting coverage. Individually optimized solutions showed a 28% gain for irradiation-focused designs and a 9% improvement for daylight-optimized configurations.
Implications for Early-Stage BIPV Façade Design
The results demonstrate that integrating parametric design with evolutionary optimization enables performance-driven decision-making for BIPV façades. The proposed framework provides a scalable and replicable methodology that supports architects and engineers in developing façade systems that are both energy-efficient and daylight-responsive, particularly during early design phases.
Comments
Post a Comment