Noninvasive Detection of Appliance Utilization Patterns in Residential Electricity Demand

Abstract

Smart meters with automatic meter reading functionalities are becoming popular across the world. As a result, load measurements at various sampling frequencies are now available. Several methods have been proposed to infer device usage characteristics from household load measurements. However, many techniques are based on highly intensive computations that incur heavy computational costs; moreover, they often rely on private household information. In this paper, we propose a technique for the detection of appliance utilization patterns using low-computational-cost algorithms that do not require any information about households. Appliance utilization patterns are identified only from the system status behavior, represented by large system status datasets, by using dimensionality reduction and clustering algorithms. Principal component analysis, k-means, and the elbow method are used to define the clusters, and the minimum spanning tree is used to visualize the results that show the appearance of utilization patterns. Self organizing maps are used to create a system status classifier. We applied our techniques to two public datasets from two different countries, the United Kingdom (UK-DALE) and the US (REDD), with different usage patterns. The proposed clustering techniques enable effective demand-side management, while the system status classifier can detect appliance malfunctions only through system status analyses.

Enhancing NILM classification via robust principal component analysis dimension reduction

“…In [15] , PCA dimensionality reduction was applied as an unsupervised NILM approach to identify power consumption patterns of home electrical appliances. In [16] , PCA and k-means were used to detect the presence of appliance clusters, alongside a method to identify the appliances withing each cluster, followed by a minimum spanning tree as a dimension reduction for easier interpretation of the identified clusters. In [17] , several pattern recognition algorithms for residential energy disaggregation were evaluated, including decision trees, support vector machine, optimum-path forest, multilayer perceptron, and k-nearest neighbors.…”