Short-Term Load Forecasting for Smart Grid based on Bidirectional-LSTM Recurrent Neural Network

Short-Term Load Forecasting for Smart Grid based on Bidirectional-LSTM Recurrent Neural Network | 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 Load Forecasting for Smart Grid based on Bidirectional-LSTM Recurrent Neural Network Saima Zafar, Shahwaiz Ahmed Hashmi, Rana Hamza Ayub, Hasan Farooq This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-5458984/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 The traditional power grid is evolving into a smart grid, integrating advanced two-way communication technologies and a greater proportion of renewable energy sources, resulting in a more dynamic and flexible network. Accurate load forecasting is crucial for effective operation, planning, and management of the smart grid. Short-term load forecasting (STLF) is particularly challenging due to the high variability and unpredictability in individual consumer behavior, which can impact forecasting accuracy and complicate daily operations and scheduling. Advanced deep learning techniques offer a promising solution to this problem by improving the accuracy of STLF. In this paper, we introduce an ensemble forecasting framework that combines the convolutional neural network (CNN) with a bidirectional long short-term memory (BiLSTM) recurrent neural network with dynamic weight adjustment (DWA). The CNN layers extract features from the data, the DWA layer multiplies the extracted features by their respective dynamic weights before passing them to the BiLSTM model which enhances the forecasting accuracy by capturing both past and future temporal dependencies. We evaluate this framework using a high-resolution real residential smart meter readings dataset and compare its performance against standalone and hybrid models. Our results demonstrate that the BiLSTM-based framework outperforms LSTM-based and traditional approaches in key metrics, including mean absolute percentage error (MAPE) with an improvement of MAPE by 1.99% against the benchmark CNN-LSTM model. This underscores our model's superior accuracy and reliability for STLF, marking a significant advancement over traditional methods. Our model effectively enhances forecasting accuracy in smart grid applications. Artificial neural networks load scheduling recurrent neural networks short-term load forecasting smart grid 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-5458984","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":379530754,"identity":"3011ac43-f23a-4dc9-a66a-3b24f05043fe","order_by":0,"name":"Saima Zafar","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA9ElEQVRIiWNgGAWjYFACxgYwxd7AwPgAzDoAFyOghecAA7MBkVqgAKiFTYIoLfLth1s3/GCok+dhP/usurCNQY7vRgLbwxl4tBicSWy72cPAZtjDk252e2Ybg7HkjQR2ww34tDAktt3gYeBh3M+Qxnabt40hcQPQFskH+BzW/7Dt5h8GCfse/mdsxUAt9QS1MNxIbLvNw2CQ2CORxsYM1JJgANKC12E3HrbdljFISO6ReMYszXNOwnDmmYfthvi8L9+f/uzmm4o62x7+NMbPPGU28nzHk4897MHnMIhdcBYoahjbCGrAAGykaxkFo2AUjILhDAC3t0vPt74VFwAAAABJRU5ErkJggg==","orcid":"","institution":"National University of Computer and Emerging Sciences","correspondingAuthor":true,"prefix":"","firstName":"Saima","middleName":"","lastName":"Zafar","suffix":""},{"id":379530755,"identity":"1125ae0b-7d08-4cb1-9ca0-c44c5432c80a","order_by":1,"name":"Shahwaiz Ahmed Hashmi","email":"","orcid":"","institution":"Vanderbilt University","correspondingAuthor":false,"prefix":"","firstName":"Shahwaiz","middleName":"Ahmed","lastName":"Hashmi","suffix":""},{"id":379530756,"identity":"fa682554-2683-475c-ab0f-f76da7ab76be","order_by":2,"name":"Rana Hamza Ayub","email":"","orcid":"","institution":"National University of Computer and Emerging Sciences","correspondingAuthor":false,"prefix":"","firstName":"Rana","middleName":"Hamza","lastName":"Ayub","suffix":""},{"id":379530757,"identity":"5eaaed13-f482-4f79-b5db-9c907e132338","order_by":3,"name":"Hasan Farooq","email":"","orcid":"","institution":"National University of Computer and Emerging Sciences","correspondingAuthor":false,"prefix":"","firstName":"Hasan","middleName":"","lastName":"Farooq","suffix":""}],"badges":[],"createdAt":"2024-11-15 08:38:37","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-5458984/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-5458984/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":73942450,"identity":"2fd177a9-eb0c-4897-a21c-95a64510a998","added_by":"auto","created_at":"2025-01-16 08:09:35","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":586372,"visible":true,"origin":"","legend":"","description":"","filename":"EnergyEfficiencyManuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-5458984/v1_covered_ea21e207-0934-4f41-963f-578967a2cc8e.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Short-Term Load Forecasting for Smart Grid based on Bidirectional-LSTM Recurrent Neural Network","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":"Artificial neural networks, load scheduling, recurrent neural networks, short-term load forecasting, smart grid","lastPublishedDoi":"10.21203/rs.3.rs-5458984/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-5458984/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eThe traditional power grid is evolving into a smart grid, integrating advanced two-way communication technologies and a greater proportion of renewable energy sources, resulting in a more dynamic and flexible network. 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