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The 2030 Challenge is aimed at making all new buildings and major renovationscarbon neutral by 2030. One of the potential solutions to meet this challengeis through innovative sustainable design strategies. For developing suchstrategies it is important to understand how the various building factorscontribute to energy usage of a building, right at design time. The growth ofartificial intelligence (AI) in recent years provides an unprecedentedopportunity to advance sustainable design by learning complex relationshipsbetween building factors from available data. However, rich training datasetsare needed for AI-based solutions to achieve good prediction accuracy.Unfortunately, obtaining training datasets are time consuming and expensive inmany real-world applications. Motivated by these reasons, we address theproblem of accurately predicting the energy usage of new or unknown buildingtypes, i.e., those building types that do not have any training data. Wepropose a novel approach based on zero-shot learning (ZSL) to solve thisproblem. Our approach uses side information from building energy modelingexperts to predict the closest building types for a given new/unknown buildingtype. We then obtain the predicted energy usage for the k-closest buildingtypes using the models learned during training and combine the predicted valuesusing a weighted averaging function. We evaluated our approach on a datasetcontaining five building types generated using BuildSimHub, a popular platformfor building energy modeling. Our approach achieved better average accuracythan a regression model (based on XGBoost) trained on the entire dataset ofknown building types.

Zero Shot Learning for Predicting Energy Usage of Buildings in Sustainable Design