{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,4]],"date-time":"2026-05-04T16:03:37Z","timestamp":1777910617718,"version":"3.51.4"},"reference-count":29,"publisher":"SAGE Publications","issue":"3","license":[{"start":{"date-parts":[[2020,9,4]],"date-time":"2020-09-04T00:00:00Z","timestamp":1599177600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/journals.sagepub.com\/page\/policies\/text-and-data-mining-license"}],"funder":[{"DOI":"10.13039\/501100010880","name":"state grid corporation of china","doi-asserted-by":"publisher","id":[{"id":"10.13039\/501100010880","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["journals.sagepub.com"],"crossmark-restriction":true},"short-container-title":["Transactions of the Institute of Measurement and Control"],"published-print":{"date-parts":[[2021,2]]},"abstract":"<jats:p>Non-intrusive load monitoring (NILM) is a critical technique for advanced smart grid management due to the convenience of monitoring and analysing individual appliances\u2019 power consumption in a non-intrusive fashion. Inspired by emerging machine learning technologies, many recent non-intrusive load monitoring studies have adopted artificial neural networks (ANN) to disaggregate appliances\u2019 power from the non-intrusive sensors\u2019 measurements. However, back-propagation ANNs have a very limit ability to disaggregate appliances caused by the great training time and uncertainty of convergence, which are critical flaws for low-cost devices. In this paper, a novel self-organizing probabilistic neural network (SPNN)-based non-intrusive load monitoring algorithm has been developed specifically for low-cost residential measuring devices. The proposed SPNN has been designed to estimate the probability density function classifying the different types of appliances. Compared to back-propagation ANNs, the SPNN requires less iterative synaptic weights update and provides guaranteed convergence. Meanwhile, the novel SPNN has less space complexity when compared with conventional PNNs by the self-organizing mechanism which automatically edits the neuron numbers. These advantages make the algorithm especially favourable to low-cost residential NILM devices. The effectiveness of the proposed algorithm is demonstrated through numerical simulation by using the public REDD dataset. Performance comparisons with well-known benchmark algorithms have also been provided in the experiment section.<\/jats:p>","DOI":"10.1177\/0142331220950865","type":"journal-article","created":{"date-parts":[[2020,9,4]],"date-time":"2020-09-04T03:41:26Z","timestamp":1599190886000},"page":"635-645","update-policy":"https:\/\/doi.org\/10.1177\/sage-journals-update-policy","source":"Crossref","is-referenced-by-count":6,"title":["Self-organizing probability neural network-based intelligent non-intrusive load monitoring with applications to low-cost residential measuring devices"],"prefix":"10.1177","volume":"43","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-0252-6765","authenticated-orcid":false,"given":"Zejian","family":"Zhou","sequence":"first","affiliation":[{"name":"GEIRI North America, USA"},{"name":"Electrical and Biomedical Engineering Department, University of Nevada Reno, 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