{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,13]],"date-time":"2026-02-13T17:17:30Z","timestamp":1771003050572,"version":"3.50.1"},"reference-count":15,"publisher":"SAGE Publications","issue":"3","license":[{"start":{"date-parts":[[2024,12,24]],"date-time":"2024-12-24T00:00:00Z","timestamp":1734998400000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/journals.sagepub.com\/page\/policies\/text-and-data-mining-license"}],"funder":[{"name":"The Industrial-Grade Intelligent Load Monitoring Technology Service Based on Demand-Side Response Project","award":["SGHANYXCYWJS2310162"],"award-info":[{"award-number":["SGHANYXCYWJS2310162"]}]}],"content-domain":{"domain":["journals.sagepub.com"],"crossmark-restriction":true},"short-container-title":["Journal of Computational Methods in Sciences and Engineering"],"published-print":{"date-parts":[[2025,5]]},"abstract":"<jats:p>To tackle the challenges posed by the limited volume of current electric load datasets and the inability of non-intrusive load monitoring algorithms to effectively balance accuracy and efficiency, this paper proposes a deep learning-based pre-classification multi-model fusion approach for non-intrusive load identification. Initially, a dynamic harmonic admittance model is employed to generate diverse, complex, and more realistic electric load data, enhancing the representativeness of the dataset. This generated data is subsequently fed into a correction network for calibration, ensuring that the data aligns closely with real-world scenarios. Building on this foundation, a pre-classification load identification fusion model is constructed, utilizing decision trees to effectively categorize electric loads. The proposed pre-classification multi-model fusion non-intrusive load identification algorithm integrates convolutional neural networks (CNN) and Transformer architectures, achieving a balanced trade-off between identification accuracy and operational efficiency. The overall performance of the algorithm was rigorously evaluated using a real dataset that was supplemented with model-generated data. Experimental results demonstrate that the proposed method significantly enhances identification accuracy and outperforms existing algorithms in the field. This research not only contributes a novel approach to load identification but also paves the way for more effective non-intrusive monitoring solutions in various applications.<\/jats:p>","DOI":"10.1177\/14727978241310540","type":"journal-article","created":{"date-parts":[[2025,5,18]],"date-time":"2025-05-18T23:41:27Z","timestamp":1747611687000},"page":"2150-2158","update-policy":"https:\/\/doi.org\/10.1177\/sage-journals-update-policy","source":"Crossref","is-referenced-by-count":1,"title":["Pre-classification-based multi-model fusion non-intrusive load identification"],"prefix":"10.1177","volume":"25","author":[{"given":"Demin","family":"Yuan","sequence":"first","affiliation":[{"name":"State Grid Henan Xichuan County Power Supply Company"}]},{"given":"Xingdong","family":"Zhang","sequence":"additional","affiliation":[{"name":"State Grid Henan Xichuan County Power Supply Company"}]},{"given":"JianGang","family":"Jia","sequence":"additional","affiliation":[{"name":"State Grid Henan Xichuan County Power Supply Company"}]},{"given":"Jianbo","family":"Zhang","sequence":"additional","affiliation":[{"name":"State Grid Henan Xichuan County Power Supply Company"}]},{"given":"Ping","family":"Lu","sequence":"additional","affiliation":[{"name":"State Grid Henan Xichuan County Power Supply Company"}]},{"given":"Juntao","family":"Wang","sequence":"additional","affiliation":[{"name":"State Grid Henan Xichuan County Power Supply Company"}]},{"given":"Shiya","family":"Li","sequence":"additional","affiliation":[{"name":"State Grid Henan Xichuan County Power Supply Company"}]},{"given":"Zhengmin","family":"Kong","sequence":"additional","affiliation":[{"name":"The Department of Artificial Intelligence and Automation, School of Electrical Engineering and Automation, Wuhan University, Wuhan, China"}]}],"member":"179","published-online":{"date-parts":[[2024,12,24]]},"reference":[{"key":"e_1_3_2_2_2","doi-asserted-by":"crossref","unstructured":"Xiang L Guo Y Yan M et al. Non-intrusive load monitoring for consistent shape loads based on convolutional neural network. In 2022 IEEE 5th international conference on computer and communication engineering technology (CCET) Beijing 2022 pp. 202\u2013206.","DOI":"10.1109\/CCET55412.2022.9906390"},{"key":"e_1_3_2_3_2","doi-asserted-by":"crossref","unstructured":"Kumar Sonwani P Swarnkar M Singh G et al. A review on non-intrusive load monitoring. In 2023 international conference on power instrumentation energy and control (PIECON) Aligarh 2023 pp. 1\u20134.","DOI":"10.1109\/PIECON56912.2023.10085808"},{"key":"e_1_3_2_4_2","doi-asserted-by":"crossref","unstructured":"Cheng H Ouyang Z Ding Z et al. Development of a smart meter with non- intrusive load monitoring function. In 2022 5th international conference on energy electrical and power engineering (CEEPE) Chongqing 2022 pp. 822\u2013826.","DOI":"10.1109\/CEEPE55110.2022.9783385"},{"key":"e_1_3_2_5_2","doi-asserted-by":"publisher","DOI":"10.1109\/5.192069"},{"key":"e_1_3_2_6_2","doi-asserted-by":"crossref","unstructured":"Zhi D Shi J Fu R. Algorithm implementation of non-intrusive load monitoring based on load core feature identification. In 2022 5th international conference on energy electrical and power engineering (CEEPE) Chongqing 2022 pp. 839\u2013844.","DOI":"10.1109\/CEEPE55110.2022.9783238"},{"key":"e_1_3_2_7_2","doi-asserted-by":"crossref","unstructured":"Cole AI Albicki A. Algorithm for nonintrusive identification of residential appliances. In 1998 IEEE international symposium on circuits and systems (ISCAS) Monterey CA 1998 pp. 338\u2013341.","DOI":"10.1109\/ISCAS.1998.704019"},{"key":"e_1_3_2_8_2","doi-asserted-by":"crossref","unstructured":"Chang H-H Chen K-L Tsai Y-P et al. A new measurement method for power signatures of non- intrusive demand monitoring and load identification. In 2011 IEEE industry applications society annual meeting Orlando FL 2011 pp. 1\u20137.","DOI":"10.1109\/IAS.2011.6074429"},{"key":"e_1_3_2_9_2","doi-asserted-by":"publisher","DOI":"10.1109\/JSEN.2022.3155883"},{"key":"e_1_3_2_10_2","doi-asserted-by":"publisher","DOI":"10.1109\/61.489367"},{"key":"e_1_3_2_11_2","doi-asserted-by":"crossref","unstructured":"Shridivia Nuran A Murti MA Suratman FY. Non-intrusive load monitoring method for appliance identification using random forest algorithm. In 2023 IEEE 13th annual computing and communication workshop and conference (CCWC) Las Vegas NV 2023 pp. 754\u2013758.","DOI":"10.1109\/CCWC57344.2023.10099248"},{"key":"e_1_3_2_12_2","doi-asserted-by":"crossref","unstructured":"Lu C Ma L Xu T et al. Non-intrusive load monitoring method based on improved differential evolution algorithm. In 2019 11th international conference on measuring technology and mechatronics automation (ICMTMA) Qiqihar 2019 pp. 279\u2013283.","DOI":"10.1109\/ICMTMA.2019.00069"},{"key":"e_1_3_2_13_2","doi-asserted-by":"crossref","unstructured":"Lu L Yu D Lin P et al. Non-intrusive load monitoring method based on BIC event detection and LSTM network model. In 2022 3rd international conference on advanced electrical and energy systems (AEES) Lanzhou 2022 pp. 238\u2013242.","DOI":"10.1109\/AEES56284.2022.10079312"},{"key":"e_1_3_2_14_2","doi-asserted-by":"publisher","DOI":"10.1109\/TGCN.2021.3087702"},{"key":"e_1_3_2_15_2","doi-asserted-by":"publisher","DOI":"10.1109\/TSG.2022.3191908"},{"key":"e_1_3_2_16_2","doi-asserted-by":"crossref","unstructured":"Azzam A Sanami S Aghdam AG. Low-frequency load identification using CNN-BiLSTM attention mechanism. In 2024 32nd Mediterranean conference on control and automation (MED) Chania - Crete 2024 pp. 712\u2013717.","DOI":"10.1109\/MED61351.2024.10566167"}],"container-title":["Journal of Computational Methods in Sciences and Engineering"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/journals.sagepub.com\/doi\/pdf\/10.1177\/14727978241310540","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/journals.sagepub.com\/doi\/full-xml\/10.1177\/14727978241310540","content-type":"application\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/journals.sagepub.com\/doi\/pdf\/10.1177\/14727978241310540","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,2,13]],"date-time":"2026-02-13T16:31:19Z","timestamp":1771000279000},"score":1,"resource":{"primary":{"URL":"https:\/\/journals.sagepub.com\/doi\/10.1177\/14727978241310540"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,12,24]]},"references-count":15,"journal-issue":{"issue":"3","published-print":{"date-parts":[[2025,5]]}},"alternative-id":["10.1177\/14727978241310540"],"URL":"https:\/\/doi.org\/10.1177\/14727978241310540","relation":{},"ISSN":["1472-7978","1875-8983"],"issn-type":[{"value":"1472-7978","type":"print"},{"value":"1875-8983","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,12,24]]}}}