A multi-objective optimization approach for climate-adaptive building envelope design using parametric behavior maps

This research presents a multi-objective optimization (MOO) framework to support the climate-adaptive building envelope (CABE) design decisionmaking process using a parametric behavior map (PBM). Unlike static shading, CABE systems include dynamic operations that significantly affect their performance; thus, well-informed strategies for scheduling dynamic operations should be integrated to analyze CABE performance. In this study, two conflicting objectives were pursued: minimizing cooling load and maximizing daylighting performance during the summer season in a hot and humid climate (Houston, Texas). Variables in the CABE performance optimization process were defined as dynamic operation schedules having either parametric linear or non-linear relationships between the degree of openness of the CABE model and certain weather stimuli (i.e., solar radiation). Two CABE models were tested with the PBM by integrating a parametric non-linear function that efficiently conducted the optimization process in a large search space. The outcomes of this optimization study included Pareto-front solutions such as optimal CABE performance and their dynamic operation scenarios. These optimal operation scenarios were determined based on the CABE design options available and user's desired objectives; in some cases, static scenarios were found to be superior. Ultimately, combining PBM with a MOO framework will contribute to the field of performance-based CABE design by supporting architects and engineers and facilitating better decisions through well-informed dynamic operation scenarios.