Density And Dynamic Time Warping Based Spatial Clustering For Appliance Operation Modes

Abstract

Household demand response (DR) is an important research problem that aims to modify consumer’s energy consumption. One of the promising areas is clustering Appliance Operation Modes (AOMs) and inducing DR by promoting consumption patterns that use less energy-intensive modes. This work proposes a novel clustering approach (DDTWSC) which aims to cluster AOMs based on the similarity of the appliance load profiles (SUPs). DDTWSC leverages the power of the DensityBased Spatial Clustering of Applications with Noise (DBSCAN) algorithm to partition the appliance load profiles into clusters of similar profiles that share the same AOM. Within DBSCAN, to measure the similarity between SUPs, the Dynamic Time Warping (DTW) algorithm is used. The resulting clustering is evaluated against two publicly datasets, namely RAE and UK-DALE. The Silhouette score is used to measure the performance of the proposed technique in clustering SUPs. DDTWSC demonstrated a significant improvement in the results compared to similar previous work in the literature.