Power User Load Forecasting Based on Spearman-ICEEMDAN-TCN-iTransformer

Power User Load Forecasting Based on Spearman-ICEEMDAN-TCN-iTransformer | 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 Research Article Power User Load Forecasting Based on Spearman-ICEEMDAN-TCN-iTransformer Jialin Zhou, Miaoheng Yang, Wei Hu, Puliang Du This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7532182/v1 This work is licensed under a CC BY 4.0 License Status: Under Revision Version 1 posted 9 You are reading this latest preprint version Abstract To improve the accuracy and dynamic adaptability of power user load forecasting, this study conducts research on load forecasting based on multi-technology integration. Firstly, Spearman rank correlation analysis is used to construct a multi-modal feature set, and the correlation of multi-modal features is quantified to achieve effective fusion of multi-source heterogeneous data such as meteorological data and electricity price data. Secondly, an improved ICEEMDAN algorithm is introduced to decompose and reconstruct the load sequence. Adaptive noise injection is used to optimize time-frequency feature extraction, and a hybrid TCN-iTransformer architecture is combined to capture short-term fluctuations and long-term trends. This provides theoretical and methodological support for accurate load forecasting in the new power system. load forecasting power users feature engineering deep learning decomposition algorithm Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Under Revision Version 1 posted Editorial decision: Revision requested 05 Oct, 2025 Reviews received at journal 13 Sep, 2025 Reviewers agreed at journal 06 Sep, 2025 Reviewers agreed at journal 04 Sep, 2025 Reviewers agreed at journal 04 Sep, 2025 Reviewers invited by journal 04 Sep, 2025 Editor assigned by journal 04 Sep, 2025 Submission checks completed at journal 04 Sep, 2025 First submitted to journal 04 Sep, 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. 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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-7532182","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":511278243,"identity":"cef3cf22-396f-4eef-88ba-1cc1beb55ac9","order_by":0,"name":"Jialin Zhou","email":"","orcid":"","institution":"School of Economics and Management, Shanghai University of Electric Power","correspondingAuthor":false,"prefix":"","firstName":"Jialin","middleName":"","lastName":"Zhou","suffix":""},{"id":511278244,"identity":"98b284f9-363e-4b49-901d-a6a8a1ba5689","order_by":1,"name":"Miaoheng Yang","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA6ElEQVRIie3RsUoDQRCA4VkGJs3gtRMIySscHARBwcIX2SNwVkLKKwKeIEnhydW+RUrLCQdbbbBNmeALeF3sTK9kz85iv3p+dmcXIIr+IUqa7vB5vH5o4L3b23IRTi5Es2xYF+b1UbN07104GYOdClNr1q1Oh4cn7HExUCvCLYLToswrgmT1bM8nWGk6v7wjU6vb5W8jEL9dB07ZVFb4ilE2y13uCVK5DyUzUCYUmnzQPF9in6QwFdNtyuAI+iXiEU+PbAU8ivWOg7tMmnrwdfpKe6PedMdyMU5WL+eTH/hv41EURdGvvgE7JUnwNOUlawAAAABJRU5ErkJggg==","orcid":"","institution":"School of Economics and Management, Shanghai University of Electric Power","correspondingAuthor":true,"prefix":"","firstName":"Miaoheng","middleName":"","lastName":"Yang","suffix":""},{"id":511278245,"identity":"763daa96-216a-47e7-b21e-c513d4d13b8b","order_by":2,"name":"Wei Hu","email":"","orcid":"","institution":"School of Economics and Management, Shanghai University of Electric Power","correspondingAuthor":false,"prefix":"","firstName":"Wei","middleName":"","lastName":"Hu","suffix":""},{"id":511278246,"identity":"81d540bd-ee33-4923-8402-99923cedeeed","order_by":3,"name":"Puliang Du","email":"","orcid":"","institution":"School of Economics and Management, Shanghai University of Electric Power","correspondingAuthor":false,"prefix":"","firstName":"Puliang","middleName":"","lastName":"Du","suffix":""}],"badges":[],"createdAt":"2025-09-04 05:08:23","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-7532182/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-7532182/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":91069145,"identity":"91790d77-0258-49a3-ad7d-20f8bf2425e9","added_by":"auto","created_at":"2025-09-11 10:23:59","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2594933,"visible":true,"origin":"","legend":"","description":"","filename":"PowerUserLoadForecastingBasedonSpearman.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7532182/v1_covered_5f9e8900-f9b0-4c80-82c3-533195efe360.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Power User Load Forecasting Based on Spearman-ICEEMDAN-TCN-iTransformer","fulltext":[],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":false,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":true,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":true,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":false,"email":"","identity":"sustainable-energy-research","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"","title":"Sustainable Energy Research","twitterHandle":"","acdcEnabled":false,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"VoR Journals","inReviewEnabled":false,"inReviewRevisionsEnabled":false},"keywords":"load forecasting, power users, feature engineering, deep learning, decomposition algorithm","lastPublishedDoi":"10.21203/rs.3.rs-7532182/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7532182/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eTo improve the accuracy and dynamic adaptability of power user load forecasting, this study conducts research on load forecasting based on multi-technology integration. 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