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Buildings account for 40% of total energy demand in the US. Consequently, there is a pressing need for a dataset that provides comprehensive information on the energy consumption of household units in the US. The current practice on large-scale energy simulations may not reflect the actual energy consumption patterns. Additionally, the existing national building energy datasets, such as the RECS, have a limited number of datapoint and do not reflect the social aspects of the households. This study aimed to create a large-scale national residential building energy dataset using a two-stage machine learning approach, combining two national datasets of the RECS and the AHS. The outcome of this study is a large-scale and comprehensive national dataset that contains information about energy consumption in household units as well as their detailed building features. Three machine learning algorithms …