Behavior-Sensitive Multi-Objective Optimization for Residential Energy-Saving Design
Traditional building energy simulation models often overlook the stochastic behavior of occupants and the complex interactions between multiple devices. This limitation creates a significant gap between predicted and actual building energy consumption. Addressing this issue requires behavior-sensitive frameworks that integrate both human and technical dimensions of building performance. The proposed study bridges this gap by introducing a fuzzy multi-criteria decision-making (FMCDM) approach coupled with evolutionary optimization to ensure realistic and adaptive performance predictions.
Significance of Occupant Behavior in Energy Modeling
Occupant behavior is one of the most influential yet uncertain factors in determining building energy performance. Stochastic patterns, such as irregular use of appliances, varying thermal preferences, and diverse daily routines, make deterministic models insufficient. Incorporating behavioral diversity through FMCDM provides more accurate results, enabling energy strategies that reflect real-world conditions rather than oversimplified assumptions.Multi-Objective Optimization Using NSGA-II
The Non-dominated Sorting Genetic Algorithm II (NSGA-II) plays a central role in exploring trade-offs between competing objectives. In this study, the algorithm optimizes building envelope parameters with three goals: reducing per-unit-area energy consumption, minimizing energy disparity across residential units, and lowering discomfort hours. The approach highlights the power of evolutionary computation in addressing the complexity of sustainable building design.Decision-Making with Entropy-Weighted TOPSIS
From the Pareto front generated by NSGA-II, entropy-weighted TOPSIS is applied to rank and select optimal solutions. This method considers the diversity of occupant thermal and energy preferences, ensuring that final design recommendations balance both efficiency and comfort. By incorporating decision-making models, the framework ensures transparency and adaptability in identifying the most suitable building configurations.Key Findings and Performance Outcomes
The study demonstrates measurable improvements when shifting from deterministic to behavior-sensitive modeling. Results include an 8.95% reduction in per-unit-area energy consumption, a 63% decrease in inter-unit energy differences, and only a marginal 2% increase in annual discomfort hours. These outcomes highlight the effectiveness of integrating occupant behavior and decision-making models in enhancing building energy performance without compromising comfort.
Correlation Insights on Design Parameters
Correlation and partial correlation analyses reveal that design factors such as insulation thickness and solar heat gain coefficient (SHGC) of windows strongly influence both energy use and comfort levels. Understanding these relationships is crucial for guiding architects, engineers, and policymakers in formulating strategies that balance efficiency with occupant satisfaction. These findings confirm the need for integrated approaches that link technical performance with human-centric considerations.Architecture Engineers Awards
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