Multi-objective optimization of residential energy-saving design based on fuzzy multi-criteria decision-making behavior model
Suzhen Pei, Gaoxiang Chen, Jian Yao, Yichen Dang
Abstract
Traditional building energy simulation models often neglect the stochastic nature of occupant behavior and multi-device interaction, leading to discrepancies between predicted and actual energy consumption. This study introduces a multi-objective optimization framework based on a Fuzzy Multi-Criteria Decision-Making (FMCDM) behavioral model. The NSGA-II algorithm is applied to optimize building envelope configurations, targeting three objectives: minimizing per-unit-area energy consumption, reducing energy disparities across residential units, and lowering annual discomfort hours. To select optimal solutions from the Pareto front, an entropy-weighted TOPSIS method is employed, reflecting varied occupant thermal and energy preferences. Compared to a deterministic baseline model assuming fixed occupant behavior, the proposed method achieves an 8.95 % reduction in per-unit-area energy consumption (from 32.5 to 29.6 kWh/m 2 ), a 63 % decrease in inter-unit energy differences (from 510 to 189 kWh), and a marginal increase of about 2 % in annual discomfort hours (from 286 to 292 h). Correlation and partial correlation analyses further reveal key relationships between design parameters—such as insulation thickness and window SHGC—that significantly influence both energy performance and thermal comfort. This integrated approach offers a data-driven and behavior-sensitive strategy for achieving balanced trade-offs between energy efficiency and occupant comfort in residential building design.