Nonintrusive Load Monitoring: A Temporal Multilabel Classification Approach

• Published in: IEEE transactions on industrial informatics Vol. 11; no. 1; pp. 262 - 270
• Main Authors: Basu, Kaustav, Debusschere, Vincent, Bacha, Seddik, Maulik, Ujjwal, Bondyopadhyay, Sanghamitra
• Format: Journal Article
• Language: English
• Published: Piscataway IEEE 01.02.2015; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: appliances identification; Demand side management; disaggregating algorithms; Electric utilities; energy management; Hidden Markov models; Home appliances; Informatics; machine learning; Monitoring; multi-label classification; non-intrusive load monitoring; smart grid; smart meter; Time series analysis; Training; Water heaters; smart meter; smart grid; multilabel classification; disaggregating algorithms; energy management; nonintrusive load monitoring; machine learning; Appliances identification
• ISSN: 1551-3203; 1941-0050
• DOI: 10.1109/TII.2014.2361288

The article tackles the issues related to the identification of electrical appliances inside residential buildings. Each appliance can be identified from the aggregate power readings at the meter panel. The possibility of applying a temporal multilabel classification approach in the domain of nonintrusive load monitoring is explored (nonevent-based method). A novel set of metafeatures is proposed. The method is tested on sampling rates based on the capabilities of current smart meters. The proposed approach is validated over a dataset of energy readings at residences for a period of a year for 100 houses containing different sets of appliances (water heater, washing machines, etc.). This method is applicable for the demand side management of households in the current limitation of smart meters; from the inhabitants or from the grid operator's point of view.