A New Time Series Similarity Measure and Its Smart Grid Applications

TL;DR

This paper introduces a novel time series similarity measure tailored for smart grid data, effectively capturing amplitude and temporal variations, and demonstrates its superiority over traditional measures in various smart grid applications.

Contribution

A new distance measure for electricity time series that better captures amplitude and temporal dynamics, improving smart grid data analysis tasks.

Findings

Outperforms ED and DTW in load scheduling accuracy

More effective in detecting anomalous electricity usage days

Better at identifying behind-the-meter equipment usage

Abstract

Many smart grid applications involve data mining, clustering, classification, identification, and anomaly detection, among others. These applications primarily depend on the measurement of similarity, which is the distance between different time series or subsequences of a time series. The commonly used time series distance measures, namely Euclidean Distance (ED) and Dynamic Time Warping (DTW), do not quantify the flexible nature of electricity usage data in terms of temporal dynamics. As a result, there is a need for a new distance measure that can quantify both the amplitude and temporal changes of electricity time series for smart grid applications, e.g., demand response and load profiling. This paper introduces a novel distance measure to compare electricity usage patterns. The method consists of two phases that quantify the effort required to reshape one time series into another, considering both amplitude and temporal changes. The proposed method is evaluated against ED and DTW using real-world data in three smart grid applications. Overall, the proposed measure outperforms ED and DTW in accurately identifying the best load scheduling strategy, anomalous days with irregular electricity usage, and determining electricity users' behind-the-meter (BTM) equipment.