Short-Term Forecasting in Smart Grid Environment using N-BEATS

Short-Term Forecasting in Smart Grid Environment using N-BEATS | 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 Short-Term Forecasting in Smart Grid Environment using N-BEATS Neelesh Pratap Singh, Mahamad Nabab Alam This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4116626/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 Forecasting is increasingly becoming necessary in the modern Smart Grid to properly operate and control renewable integrated power systems. Forecasting accuracy plays a vital role in the adequate scheduling of conventional resources to allow the maximum utilization of renewable sources and provide proper operation of the system. Mainly, accurate short-term forecasting of load, electricity price, solar photovoltaic (PV), and wind turbine (WT) based renewable power generations are of central importance to manage remaining sources optimally to supply cost-effective power to the consumers and reduce carbon emissions. This paper proposes a Neural Basis Expansion Analysis Time Series Forecasting (N-BEATS) algorithm for highly accurate short-term forecasting of SG load, electricity price, PV and WT-based power generations. Data preprocessing, such as normalisation, outlier management, and new cyclic feature building, is employed in the proposed algorithm to enhance its performance. The developed N-BEATS algorithm performs excellently on dynamically fluctuating datasets and peak load prediction. The suitability and effectiveness of the N-BEATS algorithm are validated on four different datasets. Further, the superiority of the algorithm is demonstrated by comparing the performance of the algorithm with several well-explored state-of-the-art forecasting algorithms. N-BEATS Deep Learning Short-Term Forecasting Smart Grid Artificial Intelligence Demand Response 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. 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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-4116626","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":280816784,"identity":"3cb46cd6-5c65-4bf6-8405-3b0ad0b9a94a","order_by":0,"name":"Neelesh Pratap Singh","email":"","orcid":"","institution":"National Institute of Technology Warangal","correspondingAuthor":false,"prefix":"","firstName":"Neelesh","middleName":"Pratap","lastName":"Singh","suffix":""},{"id":280816785,"identity":"00a4641e-07bb-48d4-87f4-be9c3f6d2386","order_by":1,"name":"Mahamad Nabab Alam","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAuklEQVRIiWNgGAWjYHACxgMMDDYMbFAeD1F6gFrSGNjYSNRymAFuDUGgO7v5wYGfOefz+OR7DBh+1DDImBPSYnbnmMHB3m23i9nYeAwYe44x8Fg2ENJyI8HgAO+224ltbLwbGHgbGHgMDhDUkv7h4N9t58BaGP8SpyXH4DDvtgNgLcxE2pJTcFh2WzJQS/6HwzLHJIhy2MaHb7fZJc5vPpb48E2NjT1BLSgAqFiCFPWjYBSMglEwCnABAG/YQPTKg8U/AAAAAElFTkSuQmCC","orcid":"","institution":"National Institute of Technology Warangal","correspondingAuthor":true,"prefix":"","firstName":"Mahamad","middleName":"Nabab","lastName":"Alam","suffix":""}],"badges":[],"createdAt":"2024-03-17 10:46:37","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4116626/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4116626/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":59139606,"identity":"88264c46-15fc-464c-ac06-03921f5d99ca","added_by":"auto","created_at":"2024-06-26 19:22:28","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1622840,"visible":true,"origin":"","legend":"","description":"","filename":"ShortTermForecasting.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4116626/v1_covered_b4600b77-948a-4101-8697-89150a1093b6.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Short-Term Forecasting in Smart Grid Environment using N-BEATS","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":"N-BEATS, Deep Learning, Short-Term Forecasting, Smart Grid, Artificial Intelligence, Demand Response","lastPublishedDoi":"10.21203/rs.3.rs-4116626/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4116626/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"Forecasting is increasingly becoming necessary in the modern Smart Grid to properly operate and control renewable integrated power systems. 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