{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,11]],"date-time":"2026-03-11T22:57:47Z","timestamp":1773269867434,"version":"3.50.1"},"reference-count":37,"publisher":"MDPI AG","issue":"14","license":[{"start":{"date-parts":[[2022,7,13]],"date-time":"2022-07-13T00:00:00Z","timestamp":1657670400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"National Key Research and Development Program of China","award":["2018YFC0604503"],"award-info":[{"award-number":["2018YFC0604503"]}]},{"name":"National Key Research and Development Program of China","award":["2008085UD06"],"award-info":[{"award-number":["2008085UD06"]}]},{"name":"National Key Research and Development Program of China","award":["201903a07020013"],"award-info":[{"award-number":["201903a07020013"]}]},{"name":"Energy Internet Joint Fund Project of Anhui province","award":["2018YFC0604503"],"award-info":[{"award-number":["2018YFC0604503"]}]},{"name":"Energy Internet Joint Fund Project of Anhui province","award":["2008085UD06"],"award-info":[{"award-number":["2008085UD06"]}]},{"name":"Energy Internet Joint Fund Project of Anhui province","award":["201903a07020013"],"award-info":[{"award-number":["201903a07020013"]}]},{"name":"Major Science and Technology Program of Anhui Province","award":["2018YFC0604503"],"award-info":[{"award-number":["2018YFC0604503"]}]},{"name":"Major Science and Technology Program of Anhui Province","award":["2008085UD06"],"award-info":[{"award-number":["2008085UD06"]}]},{"name":"Major Science and Technology Program of Anhui Province","award":["201903a07020013"],"award-info":[{"award-number":["201903a07020013"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Commercial load is an essential demand-side resource. Monitoring commercial loads helps not only commercial customers understand their energy usage to improve energy efficiency but also helps electric utilities develop demand-side management strategies to ensure stable operation of the power system. However, existing non-intrusive methods cannot monitor multiple commercial loads simultaneously and do not consider the high correlation and severe imbalance among commercial loads. Therefore, this paper proposes a deep learning-based non-intrusive commercial load monitoring method to solve these problems. The method takes the total power signal of the commercial building as input and directly determines the state and power consumption of several specific appliances. The key elements of the method are a new neural network structure called TTRNet and a new loss function called MLFL. TTRNet is a multi-label classification model that can autonomously learn correlation information through its unique network structure. MLFL is a loss function specifically designed for multi-label classification tasks, which solves the imbalance problem and improves the monitoring accuracy for challenging loads. To validate the proposed method, experiments are performed separately in seen and unseen scenarios using a public dataset. In the seen scenario, the method achieves an average F1 score of 0.957, which is 7.77% better than existing multi-label classification methods. In the unseen scenario, the average F1 score is 0.904, which is 1.92% better than existing methods. The experimental results show that the method proposed in this paper is both effective and practical.<\/jats:p>","DOI":"10.3390\/s22145250","type":"journal-article","created":{"date-parts":[[2022,7,14]],"date-time":"2022-07-14T00:12:40Z","timestamp":1657757560000},"page":"5250","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":18,"title":["Deep Learning-Based Non-Intrusive Commercial Load Monitoring"],"prefix":"10.3390","volume":"22","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-7438-0339","authenticated-orcid":false,"given":"Mengran","family":"Zhou","sequence":"first","affiliation":[{"name":"School of Electrical and Information Engineering, Anhui University of Science and Technology, Huainan 232001, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9707-3251","authenticated-orcid":false,"given":"Shuai","family":"Shao","sequence":"additional","affiliation":[{"name":"School of Electrical and Information Engineering, Anhui University of Science and Technology, Huainan 232001, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2130-3292","authenticated-orcid":false,"given":"Xu","family":"Wang","sequence":"additional","affiliation":[{"name":"School of Electrical and Information Engineering, Anhui University of Science and Technology, Huainan 232001, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7589-7786","authenticated-orcid":false,"given":"Ziwei","family":"Zhu","sequence":"additional","affiliation":[{"name":"School of Electrical and Information Engineering, Anhui University of Science and Technology, Huainan 232001, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2088-6227","authenticated-orcid":false,"given":"Feng","family":"Hu","sequence":"additional","affiliation":[{"name":"School of Electrical and Information Engineering, Anhui University of Science and Technology, Huainan 232001, China"}]}],"member":"1968","published-online":{"date-parts":[[2022,7,13]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"188","DOI":"10.1016\/j.renene.2022.03.066","article-title":"From gray to green: Energy crises and the role of CPEC","volume":"190","author":"Duan","year":"2022","journal-title":"Renew. 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