Agglomerative Hierarchical Clustering with Dynamic Time Warping for Household Load Curve Clustering

TL;DR

This paper introduces a shape-based clustering method combining Agglomerative Hierarchical Clustering with Dynamic Time Warping to classify household load curves more effectively than traditional methods, aiding demand response programs.

Contribution

The study presents a novel combination of AHC and DTW for load curve clustering, demonstrating improved performance over standard clustering algorithms.

Findings

AHC with DTW outperforms other clustering methods.

Fewer clusters are needed with the proposed approach.

The method effectively classifies residential load patterns.

Abstract

Energy companies often implement various demand response (DR) programs to better match electricity demand and supply by offering the consumers incentives to reduce their demand during critical periods. Classifying clients according to their consumption patterns enables targeting specific groups of consumers for DR. Traditional clustering algorithms use standard distance measurement to find the distance between two points. The results produced by clustering algorithms such as K-means, K-medoids, and Gaussian Mixture Models depend on the clustering parameters or initial clusters. In contrast, our methodology uses a shape-based approach that combines Agglomerative Hierarchical Clustering (AHC) with Dynamic Time Warping (DTW) to classify residential households' daily load curves based on their consumption patterns. While DTW seeks the optimal alignment between two load curves, AHC provides a realistic initial clusters center. In this paper, we compare the results with other clustering algorithms such as K-means, K-medoids, and GMM using different distance measures, and we show that AHC using DTW outperformed other clustering algorithms and needed fewer clusters.