Clustering Time-Series Energy Data from Smart Meters

TL;DR

This paper explores clustering techniques for analyzing 24-hour energy consumption patterns from smart meters, aiming to identify customer groups with similar energy usage for improved energy efficiency strategies.

Contribution

It introduces a clustering approach tailored for time-series energy data, demonstrating its effectiveness on real utility datasets for pattern recognition.

Findings

Accurate grouping of energy usage profiles

Potential application in energy efficiency programs

Effective on real-world utility data

Abstract

Investigations have been performed into using clustering methods in data mining time-series data from smart meters. The problem is to identify patterns and trends in energy usage profiles of commercial and industrial customers over 24-hour periods, and group similar profiles. We tested our method on energy usage data provided by several U.S. power utilities. The results show accurate grouping of accounts similar in their energy usage patterns, and potential for the method to be utilized in energy efficiency programs.