{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,2]],"date-time":"2026-06-02T07:17:57Z","timestamp":1780384677480,"version":"3.54.1"},"reference-count":42,"publisher":"MDPI AG","issue":"9","license":[{"start":{"date-parts":[[2021,4,30]],"date-time":"2021-04-30T00:00:00Z","timestamp":1619740800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"The Open University of Hong Kong","award":["R6394 [ROU\/E10\/20]"],"award-info":[{"award-number":["R6394 [ROU\/E10\/20]"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Global warming is a leading world issue driving the common social objective of reducing carbon emissions. People have witnessed the melting of ice and abrupt changes in climate. Reducing electricity usage is one possible method of slowing these changes. In recent decades, there have been massive worldwide rollouts of smart meters that automatically capture the total electricity usage of houses and buildings. Electricity load disaggregation (ELD) helps to break down total electricity usage into that of individual appliances. Studies have implemented ELD models based on various artificial intelligence techniques using a single ELD dataset. In this paper, a powerline noise transformation approach based on optimized complete ensemble empirical model decomposition and wavelet packet transform (OCEEMD\u2013WPT) is proposed to merge the ELD datasets. The practical implications are that the method increases the size of training datasets and provides mutual benefits when utilizing datasets collected from other sources (especially from different countries). To reveal the effectiveness of the proposed method, it was compared with CEEMD\u2013WPT (fixed controlled coefficients), standalone CEEMD, standalone WPT, and other existing works. The results show that the proposed approach improves the signal-to-noise ratio (SNR) significantly.<\/jats:p>","DOI":"10.3390\/s21093133","type":"journal-article","created":{"date-parts":[[2021,4,30]],"date-time":"2021-04-30T10:53:29Z","timestamp":1619780009000},"page":"3133","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":22,"title":["Handling Data Heterogeneity in Electricity Load Disaggregation via Optimized Complete Ensemble Empirical Mode Decomposition and Wavelet Packet Transform"],"prefix":"10.3390","volume":"21","author":[{"given":"Kwok Tai","family":"Chui","sequence":"first","affiliation":[{"name":"Department of Technology, School of Science and Technology, The Open University of Hong Kong, Hong Kong, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Brij B.","family":"Gupta","sequence":"additional","affiliation":[{"name":"Department of Computer Engineering, National Institute of Technology Kurukshetra, Kurukshetra 136119, India"},{"name":"Department of Computer Science and Information Engineering, Asia University, Taichung 41354, Taiwan"},{"name":"Macquarie University, Sydney, NSW 2109, Australia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1591-5583","authenticated-orcid":false,"given":"Ryan Wen","family":"Liu","sequence":"additional","affiliation":[{"name":"Hubei Key Laboratory of Inland Shipping Technology, School of Navigation, Wuhan University of Technology, Wuhan 430063, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Pandian","family":"Vasant","sequence":"additional","affiliation":[{"name":"Modeling Evolutionary Algorithms Simulation &amp; Artificial Intelligence (MERLIN), Faculty of Electrical &amp; Electronic Engineering, Ton Duc Thang University, Ho Chi Minh City 700000, Vietnam"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2021,4,30]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"109096","DOI":"10.1016\/j.envres.2019.109096","article-title":"The impact of permafrost on carbon dioxide and methane fluxes in Siberia: A meta-analysis","volume":"182","author":"Masyagina","year":"2020","journal-title":"Environ. 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