Multi-Objective Evolutionary Sunshade Design

Sunshades integrated into building facade design critically influence the building's thermal conditions, natural lighting, energy usage, and occupant comfort. However, heuristic designs often neglect the multi-faceted trade-offs among these objectives. This study compares two multi-objective evolutionary algorithms, NSGA-II and MO-CMA-ES, in optimizing five performance metrics: thermal comfort, Useful Daylight Illuminance (UDI), energy consumption, outside view obstruction, and cost. We integrate annual energy and daylight simulations, incorporating real-world weather data from Cape Town, South Africa, and Nairobi, Kenya. Results indicate that both MO-EAs generate Pareto-optimal sunshades exceeding the performance of five traditional designs for all metrics. In cooler climates, the best solutions featured upward-angled fins to admit beneficial solar gain, while warmer climates favored configurations blocking high-angle sunlight. These findings underscore the importance of climate-specific optimization for identifying cost-effective, occupant-friendly building designs to balance daylight management and energy efficiency.