Noninvasive Detection of Appliance Utilization Patterns in Residential Electricity Demand

Fernanda Spada Villar, Pedro Henrique Juliano Nardelli, Arun Narayanan, Renan Cipriano Moioli, Hader Azzini and Luiz Carlos Pereira da Silva
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Fernanda Spada Villar: School of Electrical and Computer Engineering, State University of Campinas, Campinas-SP 13083-852, Brazil
Pedro Henrique Juliano Nardelli: Department of Electrical Engineering, School of Energy Systems, Lappeenranta-Lahti University of Technology, Yliopistonkatu 34, 53850 Lappeenranta, Finland
Arun Narayanan: Department of Electrical Engineering, School of Energy Systems, Lappeenranta-Lahti University of Technology, Yliopistonkatu 34, 53850 Lappeenranta, Finland
Renan Cipriano Moioli: Digital Metropolis Institute, Federal University of Rio Grande do Norte, Campus Universitário, Lagoa Nova, Natal-RN 59078-900, Brazil
Hader Azzini: School of Electrical and Computer Engineering, State University of Campinas, Campinas-SP 13083-852, Brazil
Luiz Carlos Pereira da Silva: School of Electrical and Computer Engineering, State University of Campinas, Campinas-SP 13083-852, Brazil

Energies, 2021, vol. 14, issue 6, 1-23

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.

Keywords: usage pattern recognition; data mining; dimensionality reduction; k-means; principal component analysis; self organizing maps; classifiers (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2021
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