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Building energy performance is one of the key features in performance-basedbuilding design decision making. Building envelope materials can play a keyrole in improving building energy performance. The thermal properties ofbuilding materials determine the level of heat transfer through buildingenvelope, thus the annual thermal energy performance of the building. Thisresearch applies the Linear Discriminant Analysis (LDA) method to study theeffects of materials' thermal properties on building thermal loads. Twoapproaches are adopted for feature selection including the Principal ComponentAnalysis (PCA) and the Exhaustive Feature Selection (EFS). A hypotheticaldesign scenario is developed with six material alternatives for an officebuilding in Los Angeles, California. The best design alternative is selectedbased on the LDA results and the key input parameters are determined based onthe PCA and EFS methods. The PCA results confirm that among all thermalproperties of the materials, the four parameters including thermalconductivity, density, specific heat capacity, and thickness are the mostcritical features, in terms of building thermal behavior and thermal energyconsumption. This result matches quite well with the assumptions of most of thebuilding energy simulation tools.

Application of Classification and Feature Selection in Building Energy Simulations