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Engaging with performance feedback in early building design often involves building a custom parametric model and generating large datasets, which is not always feasible. Alternatively, large parametric datasets of general design problems and filtering methods could be used together to explore specific design decisions. This paper investigates the generalizability of a method that dynamically assesses variable importance and likely influence on performance objectives as a precomputed design space is filtered down. The method first trains linear model trees to predict building performance objectives across a generic design space. Leaf node models are then aggregated to provide feedback on variable importance in different design space regions. This approach is tested on three design problems that vary in number of variables, samples, and design space structure to reveal advantages and potential limitations of the method. Algorithm improvements are proposed, and general recommendations are developed to apply it on future datasets. 

Abstract Solar near-infrared (NIR) selective glazing systems have been proposed by incorporating photothermal effects (PTE) of a nanoparticle film into building windows. From an energy efficiency perspective, the nanoscale PTE forms unique inward-flowing heat by heating up the window interior surface temperature under solar near-infrared, significantly improving the window thermal performance. Also, the PTE-driven solar heat gains are dynamic upon solar radiation and weather conditions. However, the PTE on annual building energy use has not been investigated thoroughly, due to the lack of an accurate and appropriate energy simulation method. In this study, we used the EnergyPlus energy management system to develop a parametric energy model and simulation approach in which a solar-temperature-dependent thermal model was embedded into the parametric energy simulation workflow. Applying this method, we examined the solar near-infrared-dependent PTE-induced thermal performances of glazing systems and their effects on annual heating energy use in representative cold climates (i.e., Zones 4, 5, and 6). The results show that the dynamic model considering the PTE demonstrated more heating energy savings, up to 11.64% in cold climates, as opposed to the baseline model that ignored the PTE. This work presents a method to model and simulate the dynamic thermal performance of windows with PTE.