{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,22]],"date-time":"2026-01-22T01:10:39Z","timestamp":1769044239483,"version":"3.49.0"},"reference-count":59,"publisher":"MDPI AG","issue":"16","license":[{"start":{"date-parts":[[2021,8,9]],"date-time":"2021-08-09T00:00:00Z","timestamp":1628467200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"the Key Scientific Research Program of Shanghai","award":["18DZ1203305"],"award-info":[{"award-number":["18DZ1203305"]}]},{"DOI":"10.13039\/501100012166","name":"National Key Research and Development Program of China","doi-asserted-by":"publisher","award":["2019YFB2103200"],"award-info":[{"award-number":["2019YFB2103200"]}],"id":[{"id":"10.13039\/501100012166","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100012166","name":"National Key Research and Development Program of China","doi-asserted-by":"publisher","award":["2018YFB1500904"],"award-info":[{"award-number":["2018YFB1500904"]}],"id":[{"id":"10.13039\/501100012166","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Shanghai Municipal Economic and Information Commission","award":["202001015"],"award-info":[{"award-number":["202001015"]}]},{"name":"Shanghai Engineering Research Center for Artificial Intelligence  301  and Integrated Energy System","award":["19DZ2252000"],"award-info":[{"award-number":["19DZ2252000"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>To achieve the goal of carbon neutrality, the demand for energy saving by the residential sector has witnessed a soaring increase. As a promising paradigm to monitor and manage residential loads, the existing studies on non-intrusive load monitoring (NILM) either lack the scalability of real-world cases or pay unaffordable attention to identification accuracy. This paper proposes a high accuracy, ultra-sparse sample, and real-time computation based NILM method for residential appliances. The method includes three steps: event detection, feature extraction and load identification. A wavelet decomposition based standard deviation multiple (WDSDM) is first proposed to empower event detection of appliances with complex starting processes. The results indicate a false detection rate of only one out of sixteen samples and a time consumption of only 0.77 s. In addition, an essential feature for NILM is introduced, namely the overshoot multiple (which facilitates an average identification improvement from 82.1% to 100% for similar appliances). Moreover, the combination of modified weighted K-nearest neighbors (KNN) and overshoot multiples achieves 100% appliance identification accuracy under a sampling frequency of 6.25 kHz with only one training sample. The proposed method sheds light on highly efficient, user friendly, scalable, and real-world implementable energy management systems in the expectable future.<\/jats:p>","DOI":"10.3390\/s21165366","type":"journal-article","created":{"date-parts":[[2021,8,9]],"date-time":"2021-08-09T09:03:53Z","timestamp":1628499833000},"page":"5366","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":19,"title":["Non-Intrusive Load Monitoring for Residential Appliances with Ultra-Sparse Sample and Real-Time Computation"],"prefix":"10.3390","volume":"21","author":[{"given":"Minzheng","family":"Hu","sequence":"first","affiliation":[{"name":"Department of Light Sources and Illuminating Engineering, Fudan University, Shanghai 200433, China"},{"name":"Shanghai Engineering Research Center for Artificial Intelligence and Integrated Energy System, Fudan University, Shanghai 200433, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5249-432X","authenticated-orcid":false,"given":"Shengyu","family":"Tao","sequence":"additional","affiliation":[{"name":"Department of Light Sources and Illuminating Engineering, Fudan University, Shanghai 200433, China"},{"name":"Shanghai Engineering Research Center for Artificial Intelligence and Integrated Energy System, Fudan University, Shanghai 200433, China"},{"name":"Institute for Six-Sector Economy, Fudan University, Shanghai 200433, China"}]},{"given":"Hongtao","family":"Fan","sequence":"additional","affiliation":[{"name":"Department of Light Sources and Illuminating Engineering, Fudan University, Shanghai 200433, China"},{"name":"Shanghai Engineering Research Center for Artificial Intelligence and Integrated Energy System, Fudan University, Shanghai 200433, China"},{"name":"Institute for Six-Sector Economy, Fudan University, Shanghai 200433, China"}]},{"given":"Xinran","family":"Li","sequence":"additional","affiliation":[{"name":"Department of Light Sources and Illuminating Engineering, Fudan University, Shanghai 200433, China"},{"name":"Shanghai Engineering Research Center for Artificial Intelligence and Integrated Energy System, Fudan University, Shanghai 200433, China"}]},{"given":"Yaojie","family":"Sun","sequence":"additional","affiliation":[{"name":"Department of Light Sources and Illuminating Engineering, Fudan University, Shanghai 200433, China"},{"name":"Shanghai Engineering Research Center for Artificial Intelligence and Integrated Energy System, Fudan University, Shanghai 200433, China"},{"name":"Institute for Six-Sector Economy, Fudan University, Shanghai 200433, China"}]},{"given":"Jie","family":"Sun","sequence":"additional","affiliation":[{"name":"Department of Light Sources and Illuminating Engineering, Fudan University, Shanghai 200433, China"},{"name":"Shanghai Engineering Research Center for Artificial Intelligence and Integrated Energy System, Fudan University, Shanghai 200433, China"}]}],"member":"1968","published-online":{"date-parts":[[2021,8,9]]},"reference":[{"key":"ref_1","unstructured":"Nations, T.U. 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