  This paper models residential consumers' energy-consumption behavior by load
patterns and distributions and reveals the relationship between consumers' load
patterns and socioeconomic features by machine learning. We analyze the
real-world smart meter data and extract load patterns using K-Medoids
clustering, which is robust to outliers. We develop an analytical framework
with feature selection and deep learning models to estimate the relationship
between load patterns and socioeconomic features. Specifically, we use an
entropy-based feature selection method to identify the critical socioeconomic
characteristics that affect load patterns and benefit our method's
interpretability. We further develop a customized deep neural network model to
characterize the relationship between consumers' load patterns and selected
socioeconomic features. Numerical studies validate our proposed framework using
Pecan Street smart meter data and survey. We demonstrate that our framework can
capture the relationship between load patterns and socioeconomic information
and outperform benchmarks such as regression and single DNN models.
