  The wide adoption of smart meters makes residential load data available and
thus improves the understanding of the energy consumption behavior. Many
existing studies have focused on smart-meter data analysis, but the drivers of
energy consumption behaviors are not well understood. This paper aims to
characterize and estimate users' load patterns based on their demographic and
socioeconomic information. We adopt the symbolic aggregate approximation (SAX)
method to process the load data and use the K-Means method to extract key load
patterns. We develop a deep neural network (DNN) to analyze the relationship
between users' load patterns and their demographic and socioeconomic features.
Using real-world load data, we validate our framework and demonstrate the
connections between load patterns and household demographic and socioeconomic
features. We also take two regression models as benchmarks for comparisons.
