{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,7,30]],"date-time":"2025-07-30T15:03:00Z","timestamp":1753887780396,"version":"3.41.2"},"reference-count":32,"publisher":"Wiley","issue":"1","license":[{"start":{"date-parts":[[2021,1,23]],"date-time":"2021-01-23T00:00:00Z","timestamp":1611360000000},"content-version":"vor","delay-in-days":22,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":["onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["Complexity"],"published-print":{"date-parts":[[2021,1]]},"abstract":"<jats:p>Nonintrusive load monitoring in smart microgrids aims to obtain the energy consumption of individual appliances from the aggregated energy data, which is generally confronted with the error identification of the load type for energy disaggregation in microgrid energy management system (EMS). This paper proposes a classification strategy for the nonintrusive load identification scheme based on the bilateral long\u2010term and short\u2010term memory network (Bi\u2010LSTM) algorithm. The sliding window algorithm is used to extract the detected load event features and obtain the load features of data samples. In order to accurately identify these load features, the steady state information is combined as the input of the Bi\u2010LSTM model during training. Comprising long\u2010term and short\u2010term memory (LSTM) network and recurrent neural network (RNN), Bi\u2010LSTM has the advantages of stronger recognition ability. Finally, precision (<jats:italic>P<\/jats:italic>), recall (<jats:italic>R<\/jats:italic>), accuracy (<jats:italic>A<\/jats:italic>), and <jats:italic>F<\/jats:italic><jats:sub>1<\/jats:sub> values are used as the evaluation method for nonintrusive load identification. The experimental results show the accuracy of the Bi\u2010LSTM identification method for load start and stop state feature matching; moreover, the method can identify relatively low\u2010power and multistate appliances.<\/jats:p>","DOI":"10.1155\/2021\/6688889","type":"journal-article","created":{"date-parts":[[2021,1,23]],"date-time":"2021-01-23T23:20:05Z","timestamp":1611444005000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":2,"title":["A Nonintrusive Load Monitoring Method for Microgrid EMS Using Bi\u2010LSTM Algorithm"],"prefix":"10.1155","volume":"2021","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-5414-3507","authenticated-orcid":false,"given":"Dongguo","family":"Zhou","sequence":"first","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4511-1164","authenticated-orcid":false,"given":"Yangjie","family":"Wu","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1802-4321","authenticated-orcid":false,"given":"Hong","family":"Zhou","sequence":"additional","affiliation":[]}],"member":"311","published-online":{"date-parts":[[2021,1,23]]},"reference":[{"key":"e_1_2_8_1_2","doi-asserted-by":"publisher","DOI":"10.1109\/TCYB.2020.3035587"},{"key":"e_1_2_8_2_2","doi-asserted-by":"publisher","DOI":"10.1109\/tsg.2017.2703158"},{"key":"e_1_2_8_3_2","doi-asserted-by":"publisher","DOI":"10.1109\/tii.2017.2783334"},{"key":"e_1_2_8_4_2","doi-asserted-by":"publisher","DOI":"10.1109\/tii.2020.2985905"},{"key":"e_1_2_8_5_2","doi-asserted-by":"publisher","DOI":"10.1049\/iet-gtd.2016.1615"},{"key":"e_1_2_8_6_2","doi-asserted-by":"publisher","DOI":"10.1109\/access.2019.2948079"},{"key":"e_1_2_8_7_2","doi-asserted-by":"publisher","DOI":"10.1109\/access.2019.2960465"},{"key":"e_1_2_8_8_2","doi-asserted-by":"publisher","DOI":"10.1109\/access.2020.3027664"},{"key":"e_1_2_8_9_2","doi-asserted-by":"publisher","DOI":"10.1109\/tii.2017.2718666"},{"key":"e_1_2_8_10_2","doi-asserted-by":"publisher","DOI":"10.1109\/tsg.2014.2314738"},{"key":"e_1_2_8_11_2","doi-asserted-by":"publisher","DOI":"10.1109\/access.2020.2988366"},{"key":"e_1_2_8_12_2","doi-asserted-by":"publisher","DOI":"10.1109\/tce.2019.2891160"},{"key":"e_1_2_8_13_2","doi-asserted-by":"publisher","DOI":"10.1109\/tsg.2019.2918330"},{"key":"e_1_2_8_14_2","doi-asserted-by":"publisher","DOI":"10.1109\/tsg.2018.2865702"},{"key":"e_1_2_8_15_2","doi-asserted-by":"publisher","DOI":"10.1109\/access.2018.2871343"},{"key":"e_1_2_8_16_2","doi-asserted-by":"publisher","DOI":"10.1109\/access.2020.3028281"},{"key":"e_1_2_8_17_2","doi-asserted-by":"publisher","DOI":"10.1109\/tsg.2016.2532885"},{"key":"e_1_2_8_18_2","doi-asserted-by":"crossref","unstructured":"MaY. 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