Abstract
This study explores the factors that impact residential energy usage and spending in the United States. Using data from the 2020 Residential Energy Consumption Survey (RECS), we investigate the significance of different energy consumption determinants at various analysis levels. Our analysis covers residential energy usage, electricity, natural gas, propane, and fuel oil consumption. We also examine energy usage for space heating, cooling, and water heating. To leverage the extensive RECS data, which includes over 180 variables, we utilized machine learning (ML) techniques for feature selection and determined their Shapley contribution for different target outcomes. Our results indicate that the CatBoost algorithm outperforms other ML techniques on the 2020 Residential Energy Consumption Survey sample. Our findings demonstrate that it is not appropriate to aggregate consumption and expenditure, as each level has distinct important features. JEL Classification: D12, Q41, R21
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