Decomposition based CNN-RVFLN for Load Prediction and Congestion Management in Power System

Decomposition based CNN-RVFLN for Load Prediction and Congestion Management in Power System | 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 Decomposition based CNN-RVFLN for Load Prediction and Congestion Management in Power System RADHAKRISHNA DAS, Dr. Ullash Kumar Rout, Dr Prasanta Kumar Satapathy This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6224861/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 Load forecasting is commonly employed by electricity market participants to refine their trading strategies and by system operators to maintain a stable grid. Specifically, it helps system operators anticipate potential power imbalances and other critical grid conditions, allowing them to take necessary corrective measures. Forecasting critical grid conditions, such as congestion, is particularly crucial in this regard. This paper introduces a Variational Mode Decomposition based CNN-RVFLN model designed to predict hour-ahead time-series residual loads. Load forecasting is important for significant operation in power system, loads become very much uncertain because it is mostly affected by various factors. Disturbances in the load results in congestion in power system. The new age intermittent energy sources leads to various system imbalances and congestion. Therefore faster branch flow estimation is necessary for the security assessment of the power system as the transmission line is exposed to various contingencies. This paper proposes the hybrid model for load forecasting that will help in congestion management by forecasting any overloading condition in the power system. Variational Mode Decomposition CNN-RVFLN congestion management Load forecasting 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. 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-6224861","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":452250158,"identity":"5667ad88-1201-48c9-8d30-1d30f0b0d0f2","order_by":0,"name":"RADHAKRISHNA DAS","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA7ElEQVRIiWNgGAWjYNCCAww8DBJA+gMQs7GTooVxBkgLM5FaGEBamHlAHEJazGfkHv7AcOaejPzs3oOfbX5tk+djZmD88DEHtxaZG3lpEgw3inkM7pxLls7tu23YxszALDlzG24tEhI5ZkBvJ/AYSOQYSOf23GYEamFj5sWvxfgDSIv8jBzj35Y9t+2J0WIAdFgCD8ONHDNphh+3Ewlr4XmXJpFwBugwoBbL3obbyW3MjM34/cIODLEPxxLsQQ678ePPbdv57c0HP3zEo4UBGIkMCTA2YxuYbMCnHqIFAf4QUDwKRsEoGAUjEgAAT1hLSXODUOgAAAAASUVORK5CYII=","orcid":"","institution":"ODISHA UNIVERSITY OF TECHNOLOGY AND RESEARCH","correspondingAuthor":true,"prefix":"","firstName":"RADHAKRISHNA","middleName":"","lastName":"DAS","suffix":""},{"id":452250159,"identity":"1c6d6e5e-b93a-4b33-9eeb-4549c9b3e8a2","order_by":1,"name":"Dr. Ullash Kumar Rout","email":"","orcid":"","institution":"ODISHA UNIVERSITY OF TECHNOLOGY AND RESEARCH","correspondingAuthor":false,"prefix":"Dr.","firstName":"Ullash","middleName":"Kumar","lastName":"Rout","suffix":""},{"id":452250160,"identity":"527c3a9f-b9c8-4ecf-9efe-cb7eff1e32b6","order_by":2,"name":"Dr Prasanta Kumar Satapathy","email":"","orcid":"","institution":"ODISHA UNIVERSITY OF TECHNOLOGY AND RESEARCH","correspondingAuthor":false,"prefix":"Dr","firstName":"Prasanta","middleName":"Kumar","lastName":"Satapathy","suffix":""}],"badges":[],"createdAt":"2025-03-14 09:23:18","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-6224861/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6224861/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":85761304,"identity":"cfea0d21-0ec7-4a27-9db5-9947e5c666af","added_by":"auto","created_at":"2025-07-01 11:38:42","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1189819,"visible":true,"origin":"","legend":"","description":"","filename":"RANDOMVECTORFUNCTION.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6224861/v1_covered_7e846f01-8058-49b3-b054-ef1e01628ccf.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Decomposition based CNN-RVFLN for Load Prediction and Congestion Management in Power System","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":"Variational Mode Decomposition, CNN-RVFLN, congestion management, Load forecasting","lastPublishedDoi":"10.21203/rs.3.rs-6224861/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6224861/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eLoad forecasting is commonly employed by electricity market participants to refine their trading strategies and by system operators to maintain a stable grid. 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