Characterizing Residential Load Patterns by Household Demographic and Socioeconomic Factors

TL;DR

This study uses smart meter data and demographic information to identify household load patterns and analyze how socioeconomic factors influence energy consumption behaviors.

Contribution

It introduces a framework combining SAX, K-Means, and DNN to characterize load patterns and link them to demographic and socioeconomic features.

Findings

Load patterns are significantly associated with household demographics.

The proposed framework effectively predicts load patterns from demographic data.

Socioeconomic factors influence energy consumption behaviors.

Abstract

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.