{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,7,30]],"date-time":"2025-07-30T13:18:57Z","timestamp":1753881537799,"version":"3.41.2"},"reference-count":34,"publisher":"Wiley","issue":"1","license":[{"start":{"date-parts":[[2021,7,31]],"date-time":"2021-07-31T00:00:00Z","timestamp":1627689600000},"content-version":"vor","delay-in-days":211,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["61873195"],"award-info":[{"award-number":["61873195"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["Complexity"],"published-print":{"date-parts":[[2021,1]]},"abstract":"<jats:p>Integrating the nonintrusive load monitoring (NILM) technology into smart meters poses challenges in demand\u2010side management (DSM) of the smart grid when capturing detailed power information and stochastic consumption behaviours, due to the difficulties in accurately detecting load operation states in real household environments with the limited information available. In this paper, a state characteristic clustering (SCC) approach is presented for promoting the performance of event detection in NILM, which makes full use of multidimensional characteristic information. After identifying different types of state domains in an established multidimensional characteristic space, we design a sliding window difference search method (SWDS) to extract their initial clustering centre. Meanwhile, the mean\u2010shift updating and iterating procedures are conducted to find the potential terminal stable state according to the probability density function. The above control strategy considers the transient events and stable states in a time\u2010series dataset simultaneously, which thus allows the exact state of complex events to be obtained in a fluctuating environment. Moreover, a multisegment computing scheme is applied for fast computing in the state characteristic clustering process. Experiments of three different cases on both our real household dataset and REDD public dataset are provided to reveal the higher performance of the proposed SCC approach over the existing related methods.<\/jats:p>","DOI":"10.1155\/2021\/8839595","type":"journal-article","created":{"date-parts":[[2021,8,1]],"date-time":"2021-08-01T04:20:14Z","timestamp":1627791614000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":2,"title":["State Characteristic Clustering for Nonintrusive Load Monitoring with Stochastic Behaviours in Smart Grids"],"prefix":"10.1155","volume":"2021","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-8522-5935","authenticated-orcid":false,"given":"Ruotian","family":"Yao","sequence":"first","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7393-7126","authenticated-orcid":false,"given":"Hong","family":"Zhou","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5414-3507","authenticated-orcid":false,"given":"Dongguo","family":"Zhou","sequence":"additional","affiliation":[]},{"given":"Heng","family":"Zhang","sequence":"additional","affiliation":[]}],"member":"311","published-online":{"date-parts":[[2021,7,31]]},"reference":[{"key":"e_1_2_9_1_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.apenergy.2017.08.203"},{"key":"e_1_2_9_2_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.apenergy.2019.113727"},{"key":"e_1_2_9_3_2","doi-asserted-by":"publisher","DOI":"10.1109\/TII.2019.2908666"},{"key":"e_1_2_9_4_2","first-page":"2746","article-title":"Stochastic distributed secondary control for ac microgrids via event-triggered communication","volume":"11","author":"Lai J.","year":"2020","journal-title":"IEEE Transactions on Industrial Informatics"},{"key":"e_1_2_9_5_2","doi-asserted-by":"publisher","DOI":"10.1049\/iet-gtd.2016.1615"},{"key":"e_1_2_9_6_2","doi-asserted-by":"publisher","DOI":"10.1049\/iet-gtd.2018.5475"},{"key":"e_1_2_9_7_2","doi-asserted-by":"publisher","DOI":"10.1109\/tsg.2016.2620120"},{"key":"e_1_2_9_8_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.enbuild.2018.06.058"},{"key":"e_1_2_9_9_2","doi-asserted-by":"publisher","DOI":"10.1109\/TII.2020.2969458"},{"key":"e_1_2_9_10_2","unstructured":"HartG. 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