Identifying the Relationship between Seasonal Variation in Residential Load and Socioeconomic Characteristics

TL;DR

This study develops a methodology to analyze seasonal variations in residential electricity load and their relationship with socioeconomic factors using clustering and decision trees, revealing income, family size, and age as key influences.

Contribution

Introduces a combined clustering and decision tree approach to quantify seasonal load variations and link them to socioeconomic characteristics.

Findings

Income level significantly influences seasonal load patterns.

Number of children affects seasonal consumption variations.

Elderly presence impacts seasonal load changes.

Abstract

Smart meter data analysis can provide insights into residential electricity consumption behaviors. Seasonal variation in consumption is not well understood but yet important to utilities for energy pricing and services. This paper aims to develop a methodology to measure seasonal variations in load patterns and identify the relationship between seasonal variation and socioeconomic factors, as socioeconomic characteristics often have great explanatory power on electricity consumption behaviors. We first model the seasonal load patterns using a two-stage K-Medoids clustering and evaluate the relative entropy of the load pattern distributions between seasons. Then we develop decision tree classifiers for each season to analyze the importance of different socioeconomic characteristics factors. Taking real-world data as a case study, we find that income level is an essential factor influencing the pattern variation across all seasons. The number of children and the elderly is also a significant factor for certain seasonal changes.