Automatic information gain-guided convergence for refining building design parameters: Enhancing effectiveness and interpretability in simulation-based optimization

Abstract

Simulation-based optimization (SBO) is widely applied to building designs by iteratively tuning design parameters towards sustainable goals. However, numerous design parameters in exploratory stages lead to design uncertainty and exponentially increase optimization search space’s dimensionality. The non-linear, non-derivative nature of objective functions determines SBO tasks a black box, which lacks interpretability for design decisions or optimization strategies. This study introduces an Automatic Information Gain-guided Convergence (AIGGC) method for refining critical design parameters in building performance SBO. The AIGGC method extends the generic SBO process with interpretable information gain analysis for each design parameter and component, to converge to the most promising domain sub-intervals prior to traditional SBOs. Experimental results evaluated the robustness and scalability of AIGGC across two design scales. Under the same iteration budgets, AIGGC significantly enhanced three SBO algorithms, i.e., RBFOpt, CMAES, and GA, by 0.62∼0.67% less energy use intensity and 2.14∼4.74% more direct sunlight hours against the baseline solutions, respectively. The contribution of this study involves two aspects, including introducing a novel information-theory-based method for optimizing design parameters in high-dimensional SBO tasks of sustainable building designs, and a novel perspective in guiding stakeholders with interpretable analysis of building design parameters.