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A major challenge of ILM is automatic appliance identification, in which the system is able to determine automatically a label of the active appliance connected to the smart device. Existing ILM techniques depend on labels input by end-users and are usually under the supervised learning scheme. However, in reality, end-users labeling is laboriously rendering insufficient training data to fit the supervised learning models. In this work, we propose a semi-supervised learning (SSL) method that leverages rich signals from the unlabeled dataset and jointly learns the classification loss for the labeled dataset and the consistency training loss for unlabeled dataset. The samples fit into consistency learning are generated by a transformation that is built upon weighted versions of DTW Barycenter Averaging algorithm. The work is inspired by two recent advanced works in SSL in computer vision and combines the advantages of the two. We evaluate our method on the dataset collected from our developed Internet-of-Things based energy monitoring system in a smart home environment. We also examine the method\u2019s performances on 10 benchmark datasets. As a result, the proposed method outperforms other methods on our smart appliance datasets and most of the benchmarks datasets, while it shows competitive results on the rest datasets.<\/jats:p>","DOI":"10.1145\/3448415","type":"journal-article","created":{"date-parts":[[2021,6,21]],"date-time":"2021-06-21T20:19:31Z","timestamp":1624306771000},"page":"1-20","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":11,"title":["Semi-supervised Intrusive Appliance Load Monitoring in Smart Energy Monitoring System"],"prefix":"10.1145","volume":"17","author":[{"given":"Vanh Khuyen","family":"Nguyen","sequence":"first","affiliation":[{"name":"Macquarie University, Australia Macquarie Park, Sydney, Australia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Wei Emma","family":"Zhang","sequence":"additional","affiliation":[{"name":"The University of Adelaide, Australia North terrace, Adelaide, Australia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Adnan","family":"Mahmood","sequence":"additional","affiliation":[{"name":"Macquarie University, Australia Macquarie Park, Sydney, Australia"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"320","published-online":{"date-parts":[[2021,6,21]]},"reference":[{"key":"e_1_2_1_1_1","doi-asserted-by":"publisher","DOI":"10.1109\/TSG.2019.2924862"},{"key":"e_1_2_1_2_1","volume-title":"Sutton","author":"Zhang Chaoyun","year":"2018","unstructured":"Chaoyun Zhang , Mingjun Zhong , Zongzuo Wang , Nigel H. Goddard , and Charles A . Sutton . 2018 . Sequence-to-point learning with neural networks for non-intrusive load monitoring. In Proceedings of the 32nd AAAI Conference on Artificial Intelligence (AAAI\u201918), the 30th Innovative Applications of Artificial Intelligence (IAAI\u201918), and the 8th AAAI Symposium on Educational Advances in Artificial Intelligence (EAAI\u2019 18). 2604\u20132611. Chaoyun Zhang, Mingjun Zhong, Zongzuo Wang, Nigel H. Goddard, and Charles A. Sutton. 2018. Sequence-to-point learning with neural networks for non-intrusive load monitoring. In Proceedings of the 32nd AAAI Conference on Artificial Intelligence (AAAI\u201918), the 30th Innovative Applications of Artificial Intelligence (IAAI\u201918), and the 8th AAAI Symposium on Educational Advances in Artificial Intelligence (EAAI\u201918). 2604\u20132611."},{"key":"e_1_2_1_3_1","doi-asserted-by":"crossref","unstructured":"Changho Shin Sunghwan Joo Jaeryun Yim Hyoseop Lee Taesup Moon and Wonjong Rhee. 2019. 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