Effective factors for residential building energy modeling using feature engineering

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Given the improved understanding of sustainability, hundreds of factors are identified to have relevance to building energy efficiency. However, there is still a lack of knowledge about what factors play a significant role in energy consumption prediction for residential buildings. In the absence of this information, building energy consumption prediction would not be efficient. To tackle this problem, this study creates a feature engineering-based analytic framework to select effective factors for energy consumption prediction and assess their implications. Two application cases are reported to demonstrate the efficiency improvement of energy consumption prediction for residential buildings. The cases use the Residential Energy Consumption Survey database that contains more than 270 energy use-related factors about buildings and occupants in the United States. Data analysis from the two cases shows that selected features achieve 97–102% of prediction power while using 12–15% number of factors, largely reducing the dimensionality for energy prediction. The results also produce a list of significant features that are efficient predictors for residential energy modeling and evaluation at the national and regional levels. Examples of the selected features are the total number of rooms and full bathrooms, frequency of clothes dryer used, type of the housing unit, number of ceiling fans and television. The selected features explain energy use patterns and their relationships which help designer, contractors, and occupants better understand energy, behaviors, and the built environment. The resultant energy use patterns inform regional similarities, differences, and distinctive characteristics.

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Journal of Building Engineering



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