  The smart meter data analysis contributes to better planning and operations
for the power system. This study aims to identify the drivers of residential
energy consumption patterns from the socioeconomic perspective based on the
consumption and demographic data using machine learning. We model consumption
patterns by representative loads and reveal the relationship between load
patterns and socioeconomic characteristics. Specifically, we analyze the
real-world smart meter data and extract load patterns by clustering in a robust
way. We further identify the influencing socioeconomic attributes on load
patterns to improve our method's interpretability. The relationship between
consumers' load patterns and selected socioeconomic features is characterized
via machine learning models. The findings are as follows. (1) Twelve load
clusters, consisting of six for weekdays and six for weekends, exhibit a
diverse pattern of lifestyle and a difference between weekdays and weekends.
(2) Among various socioeconomic features, age and education level are suggested
to influence the load patterns. (3) Our proposed analytical model using feature
selection and machine learning is proved to be more effective than XGBoost and
conventional neural network model in mapping the relationship between load
patterns and socioeconomic features.
