Stochastic Optimization Framework for Robust Building Performance Under Occupant Behavioral Uncertainty
Optimizing building performance requires acknowledging the stochastic nature of occupant control behaviors, which significantly influence energy consumption, thermal comfort, and visual comfort outcomes. Traditional building performance models often rely on oversimplified behavioral assumptions and demand extensive computational time for stochastic simulations. This study proposes a novel optimization framework specifically designed to address uncertainty in occupant behavior while improving computational efficiency and solution robustness in building design optimization.
Methodological Integration of Stochastic and Intelligent Optimization Techniques
The proposed approach integrates Sample Average Approximation (SAA) with Monte Carlo simulations to obtain convergent mean performance estimates under uncertainty. To accelerate optimization, machine learning (ML) models are coupled with a Pareto-based Genetic Algorithm (GA), enabling rapid prediction of building performance metrics across large design spaces. This hybrid framework balances stochastic rigor with computational efficiency, ensuring reliable multi-objective optimization under variable occupant behavior scenarios.
Case Study Application in Ningbo
The framework was validated through a case study on a representative complex office building located in Ningbo, China. The building model incorporated stochastic occupant control behaviors affecting HVAC operation, lighting use, and indoor environmental conditions. The analysis assessed how behavioral variability impacts energy performance and optimization outcomes, providing a realistic context for evaluating the robustness and reliability of the proposed methodology.
Convergence Reliability and Pareto Front Performance
Results indicate that the developed optimization framework achieved approximately 2.5 times improvement in reliability when converging to the Pareto front compared to conventional stochastic optimization methods. This enhanced convergence stability demonstrates the effectiveness of combining SAA-based stochastic evaluation with ML-assisted genetic algorithms. The improved reliability reduces the risk of premature convergence and ensures a more comprehensive exploration of optimal trade-offs.
Energy Reduction and Robustness Against Parameter Sensitivity
The optimal design solutions generated through the proposed framework achieved an 11.66% reduction in energy consumption compared to baseline scenarios. Additionally, these solutions exhibited significantly lower local sensitivity to parameter perturbations, indicating greater robustness against uncertainty in occupant behavior and environmental conditions. This robustness is critical for ensuring that optimized designs maintain performance stability under real-world variability.
Multi-Criteria Decision Performance and Practical Implications
In multi-criteria decision analysis, 85% of the top 20 ranked solutions were derived from the proposed optimization approach, underscoring its superior capability in balancing energy efficiency, thermal comfort, and visual comfort objectives. The findings demonstrate that integrating stochastic modeling with intelligent optimization techniques enhances both reliability and performance quality. This framework offers a scalable and practical pathway for advancing resilient, occupant-centric building design under uncertainty.
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