Multi-source Data Fusion-based Grid-level Load Forecasting

Multi-source Data Fusion-based Grid-level Load Forecasting | 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 Multi-source Data Fusion-based Grid-level Load Forecasting Hai Ye, Xiaobi Teng, Bingbing Song, Kaiming Zou, Moyan Zhu This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-5399298/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Grid-level dispatching is generally based on the accumulation of independent load forecasting data from provincial and municipal dispatch centers. However, the differences in economic development levels and the frequency of forecasting result updates among provinces and cities lead to certain limitations in the direct accumulation method, affecting the accuracy of the integrated forecasting results. To address this, this paper proposes a short-term load forecasting method for the power grid based on the i-Transformer model. First, the dataset is constructed through data preprocessing and feature engineering, followed by training and optimizing the model parameters. Further, considering the differences in forecasting results reported by provincial dispatch centers, principal component analysis is used to determine the weights of provinces and cities, thereby effectively integrating the forecasting data from different provinces and cities through weighting. The case study shows that the i-Transformer outperforms traditional statistical and machine learning algorithms on multiple evaluation metrics, and the integration method has considerable potential in handling multi-source heterogeneous data and improving forecasting accuracy. This paper provides a new means of load forecasting result integration for power grid dispatch centers, ensuring the safe, high-quality, and economical operation of the power system. Short-term Load Forecasting Data Fusion Linear Weighting i-Transformer Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Posted Version 1 posted 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. 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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-5399298","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":378904046,"identity":"b7f25101-25c5-4896-a082-155d6fe7902f","order_by":0,"name":"Hai Ye","email":"","orcid":"","institution":"East China Branch of State Grid","correspondingAuthor":false,"prefix":"","firstName":"Hai","middleName":"","lastName":"Ye","suffix":""},{"id":378904047,"identity":"aae3e793-95f1-45c8-b3dc-5b4dd034349d","order_by":1,"name":"Xiaobi Teng","email":"","orcid":"","institution":"East China Branch of State Grid","correspondingAuthor":false,"prefix":"","firstName":"Xiaobi","middleName":"","lastName":"Teng","suffix":""},{"id":378904048,"identity":"48253113-caf0-409d-9561-6544bdec823e","order_by":2,"name":"Bingbing Song","email":"","orcid":"","institution":"East China Branch of State Grid","correspondingAuthor":false,"prefix":"","firstName":"Bingbing","middleName":"","lastName":"Song","suffix":""},{"id":378904049,"identity":"e700322f-d36e-437c-a256-84325c73360d","order_by":3,"name":"Kaiming Zou","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA1ElEQVRIiWNgGAWjYBACCTBZYcPA2ABisBGt5UwaA2MbSVoY2w5DVROjRXLa8YePedvO2zPP7zFg+FB2mIF/dgN+LdLSCcnGPOduJza28Rgwzjh3mEHizgH8WuSkE45J85TdTmAEamHmBbrQQCKBkJbENmketnP2YC1/idEiLZ3MJs3TdoAR5DBmRmK0SM5OYzaccyYZ6Je0goM959J5JG4Q0CJxO/3hgzcVdvaGzYc3PvhRZi3HP4OAFjgwbGBgOACkeYhUDwTyxCsdBaNgFIyCkQYAL0A9eVBceTwAAAAASUVORK5CYII=","orcid":"","institution":"Shanghai Jiao Tong University","correspondingAuthor":true,"prefix":"","firstName":"Kaiming","middleName":"","lastName":"Zou","suffix":""},{"id":378904050,"identity":"e6877449-3b21-4b14-9ae2-dc23f7b9f823","order_by":4,"name":"Moyan Zhu","email":"","orcid":"","institution":"Shanghai Jiao Tong University","correspondingAuthor":false,"prefix":"","firstName":"Moyan","middleName":"","lastName":"Zhu","suffix":""}],"badges":[],"createdAt":"2024-11-06 04:08:13","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-5399298/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-5399298/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":76047557,"identity":"a4d5ca16-7822-497e-810d-072ae7b259a0","added_by":"auto","created_at":"2025-02-11 19:31:34","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1129156,"visible":true,"origin":"","legend":"","description":"","filename":"MultisourceDataFusionbasedGridlevelLoadForecasting.pdf","url":"https://assets-eu.researchsquare.com/files/rs-5399298/v1_covered_354e251f-88e5-4954-8e68-e0f656118e8b.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Multi-source Data Fusion-based Grid-level Load Forecasting","fulltext":[],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":false,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":true,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":true,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Short-term Load Forecasting, Data Fusion, Linear Weighting, i-Transformer","lastPublishedDoi":"10.21203/rs.3.rs-5399298/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-5399298/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eGrid-level dispatching is generally based on the accumulation of independent load forecasting data from provincial and municipal dispatch centers. However, the differences in economic development levels and the frequency of forecasting result updates among provinces and cities lead to certain limitations in the direct accumulation method, affecting the accuracy of the integrated forecasting results. To address this, this paper proposes a short-term load forecasting method for the power grid based on the i-Transformer model. First, the dataset is constructed through data preprocessing and feature engineering, followed by training and optimizing the model parameters. Further, considering the differences in forecasting results reported by provincial dispatch centers, principal component analysis is used to determine the weights of provinces and cities, thereby effectively integrating the forecasting data from different provinces and cities through weighting. 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