Passive Shading Optimization for Buildings Based on Differential Evolution and Non-Dominated Sorting Multi-Objective Evolutionary Algorithm

Abstract

This paper proposes an hourly-scale evaluation and multi-objective optimization method for passive shading retrofits in buildings. Using typical meteorological annual data as input, the study couples solar radiation, heat transfer through building envelopes, ventilation heat exchange, window heat gain, and daylighting and glare constraints to construct a computational framework for “design variables—thermal response—energy consumption evaluation.” The study employs the Differential Evolution algorithm and the Non-Dominant Sorting Multi-Objective Evolution algorithm to obtain a Pareto optimal solution balancing energy consumption, comfort, and retrofit costs, while incorporating thermal mass coupling and adjustable shading strategies to enhance the model’s adaptability. The results indicate that the optimized solution reduces the annual cooling load by 9.4%, heating season energy consumption by 14.7%, and the number of overheating hours by 82.1%. This method is applicable to building shading retrofit decisions across different climate zones and future climate scenarios, demonstrating good universality and engineering application value.