AECF-UC: An Adaptive Electricity Consumption Forecast Approach for Universal Environments

AECF-UC: An Adaptive Electricity Consumption Forecast Approach for Universal Environments | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Article AECF-UC: An Adaptive Electricity Consumption Forecast Approach for Universal Environments Shiqi Zhou, Saisai Ni, Yifeng Han, Zhekang Dong, Chun Sing Lai This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6249659/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 08 Jul, 2025 Read the published version in Scientific Reports → Version 1 posted 10 You are reading this latest preprint version Abstract The development of an accurate electricity consumption forecast model is crucial for stable operation and intelligent management of power systems. Traditional methods often overlook user heterogeneity and lack measures to address concept drift caused by distribution changes in electricity data over time. We propose an adaptive electricity consumption probability forecasting method tailored to universal environments. The method includes a nonmonotonic correlation elimination-based recursive feature selection that adaptively determines the optimal feature combination. Our model incorporates a joint loss function combining point and probability forecasting evaluations to accurately quantify online batch errors. It also features a buffer to store batch data showing pattern changes and dynamically adjusts weights to counteract concept drift. We validated our method, adaptive electricity consumption forecast for universal environments (AECF-UC), against 7 mainstream methods using a multi-environment dataset. Comparative and ablation experiments show that AECF-UC outperforms others, achieving average RMSE and pinball scores of 0.3041 and 0.0567 respectively, with the joint loss method improving prediction accuracy by about 6% over the single-loss method. These results indicate that the proposed method exhibits certain advantages in universality and adaptability. Earth and environmental sciences/Environmental social sciences/Energy and society/Energy efficiency Earth and environmental sciences/Environmental social sciences/Energy and society/Energy management Electricity consumption forecasting concept drift probability forecasting hidden Markov model Full Text Additional Declarations No competing interests reported. Supplementary Files Highlight.docx Cite Share Download PDF Status: Published Journal Publication published 08 Jul, 2025 Read the published version in Scientific Reports → Version 1 posted Editorial decision: Revision requested 01 Apr, 2025 Reviews received at journal 29 Mar, 2025 Reviews received at journal 25 Mar, 2025 Reviewers agreed at journal 19 Mar, 2025 Reviewers agreed at journal 19 Mar, 2025 Reviewers invited by journal 19 Mar, 2025 Editor assigned by journal 19 Mar, 2025 Editor invited by journal 19 Mar, 2025 Submission checks completed at journal 18 Mar, 2025 First submitted to journal 18 Mar, 2025 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-6249659","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":433832978,"identity":"e67cfd08-2ca5-4509-bded-c4128d61c94b","order_by":0,"name":"Shiqi Zhou","email":"","orcid":"","institution":"College of electronic information, Hangzhou Dianzi University","correspondingAuthor":false,"prefix":"","firstName":"Shiqi","middleName":"","lastName":"Zhou","suffix":""},{"id":433832983,"identity":"815cf70f-88af-4657-8434-d10113435e08","order_by":1,"name":"Saisai Ni","email":"","orcid":"","institution":"College of electronic information, Hangzhou Dianzi University","correspondingAuthor":false,"prefix":"","firstName":"Saisai","middleName":"","lastName":"Ni","suffix":""},{"id":433832984,"identity":"7c6cb928-7479-446c-8c38-242d05a4515b","order_by":2,"name":"Yifeng Han","email":"","orcid":"","institution":"College of electronic information, Hangzhou Dianzi University","correspondingAuthor":false,"prefix":"","firstName":"Yifeng","middleName":"","lastName":"Han","suffix":""},{"id":433832986,"identity":"ecb4e08e-47e8-4810-b2b1-607b343de388","order_by":3,"name":"Zhekang Dong","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA4UlEQVRIiWNgGAWjYHACxgMJDAcY+CUYDIAcZuL0gLVIziBJCwgZ3CBWC790+4UDD/7cyTO+3bxNgqHCOrGB/ewBvFok55wpOJDY9qzY7M6xMgmGM+mJDTx5CXi1GNzISTiQ2HA4cduNHDMJxrbDiQ0SPAZ4tdiDtCT8OZy4eQZIyz8itBhIpB84kMB2OHGDBEhLAxFaJG7kMAD9crhY4kZasUXCsXTjNp4c/Fr4Z6Q/fPjjz+E8/hnJG298qLGW7Wc/g18LAwPEGQkMMJKNgHogYH+A0DIKRsEoGAWjABsAAPmRT3DBoSc9AAAAAElFTkSuQmCC","orcid":"","institution":"College of electronic information, Hangzhou Dianzi University","correspondingAuthor":true,"prefix":"","firstName":"Zhekang","middleName":"","lastName":"Dong","suffix":""},{"id":433832987,"identity":"a831c1af-603f-4593-b925-85f0136a98aa","order_by":4,"name":"Chun Sing Lai","email":"","orcid":"","institution":"Brunel University London","correspondingAuthor":false,"prefix":"","firstName":"Chun","middleName":"Sing","lastName":"Lai","suffix":""}],"badges":[],"createdAt":"2025-03-18 05:53:06","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-6249659/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6249659/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1038/s41598-025-10147-2","type":"published","date":"2025-07-08T15:57:39+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":86699394,"identity":"40fbcc24-61d8-4df3-9521-859311d6cc7f","added_by":"auto","created_at":"2025-07-14 16:08:51","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1855549,"visible":true,"origin":"","legend":"","description":"","filename":"ScientificReports.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6249659/v1_covered_f44d0add-6f7e-4f9b-ac61-c3528a8d1700.pdf"},{"id":79386092,"identity":"a797bfef-0ecb-4d41-9ac6-bec6c903a480","added_by":"auto","created_at":"2025-03-27 18:02:24","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":17171,"visible":true,"origin":"","legend":"","description":"","filename":"Highlight.docx","url":"https://assets-eu.researchsquare.com/files/rs-6249659/v1/0395ec681ea4c9a1d4fbbab4.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"AECF-UC: An Adaptive Electricity Consumption Forecast Approach for Universal Environments","fulltext":[],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":false,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":true,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":true,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Electricity consumption forecasting, concept drift, probability forecasting, hidden Markov model","lastPublishedDoi":"10.21203/rs.3.rs-6249659/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6249659/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eThe development of an accurate electricity consumption forecast model is crucial for stable operation and intelligent management of power systems. Traditional methods often overlook user heterogeneity and lack measures to address concept drift caused by distribution changes in electricity data over time. We propose an adaptive electricity consumption probability forecasting method tailored to universal environments. The method includes a nonmonotonic correlation elimination-based recursive feature selection that adaptively determines the optimal feature combination. Our model incorporates a joint loss function combining point and probability forecasting evaluations to accurately quantify online batch errors. It also features a buffer to store batch data showing pattern changes and dynamically adjusts weights to counteract concept drift. We validated our method, adaptive electricity consumption forecast for universal environments (AECF-UC), against 7 mainstream methods using a multi-environment dataset. Comparative and ablation experiments show that AECF-UC outperforms others, achieving average RMSE and pinball scores of 0.3041 and 0.0567 respectively, with the joint loss method improving prediction accuracy by about 6% over the single-loss method. These results indicate that the proposed method exhibits certain advantages in universality and adaptability.\u003c/p\u003e","manuscriptTitle":"AECF-UC: An Adaptive Electricity Consumption Forecast Approach for Universal Environments","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-03-27 18:02:19","doi":"10.21203/rs.3.rs-6249659/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2025-04-01T07:14:35+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-03-29T16:52:30+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-03-25T14:37:54+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"202706649319627433581076566637708941977","date":"2025-03-19T11:25:52+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"97571369626123305114587227370924324278","date":"2025-03-19T11:21:27+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-03-19T10:44:48+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-03-19T10:39:57+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2025-03-19T05:21:17+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-03-18T08:14:54+00:00","index":"","fulltext":""},{"type":"submitted","content":"Scientific Reports","date":"2025-03-18T05:23:10+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"4f5b4bc9-ef3a-4583-a1de-802201f81dd6","owner":[],"postedDate":"March 27th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[{"id":46195377,"name":"Earth and environmental sciences/Environmental social sciences/Energy and society/Energy efficiency"},{"id":46195378,"name":"Earth and environmental sciences/Environmental social sciences/Energy and society/Energy management"}],"tags":[],"updatedAt":"2025-07-14T16:02:30+00:00","versionOfRecord":{"articleIdentity":"rs-6249659","link":"https://doi.org/10.1038/s41598-025-10147-2","journal":{"identity":"scientific-reports","isVorOnly":false,"title":"Scientific Reports"},"publishedOn":"2025-07-08 15:57:39","publishedOnDateReadable":"July 8th, 2025"},"versionCreatedAt":"2025-03-27 18:02:19","video":"","vorDoi":"10.1038/s41598-025-10147-2","vorDoiUrl":"https://doi.org/10.1038/s41598-025-10147-2","workflowStages":[]},"version":"v1","identity":"rs-6249659","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6249659","identity":"rs-6249659","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}