Abstract
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.
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•A PBM was integrated into a multi-objective optimization framework.•Solar radiation values were used to define parametric operation scenarios for CABEs.•The PBM produced optimum CABE performance while considering conflicting objectives.•The PBM efficiently generated dynamic CABE operation scenarios in a MOO framework.•PBM use supports the CABE decisionmaking by offering optimum operation scenarios.