FIRE-CNN-LSTM: A Fuzzy Rough Set-Evolved Hybrid Deep Learning Model for Short-Term Load Forecasting Using Computational Intelligence

FIRE-CNN-LSTM: A Fuzzy Rough Set-Evolved Hybrid Deep Learning Model for Short-Term Load Forecasting Using Computational Intelligence | 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 Article FIRE-CNN-LSTM: A Fuzzy Rough Set-Evolved Hybrid Deep Learning Model for Short-Term Load Forecasting Using Computational Intelligence Franck-Steve KAMDEM KENGNE, Mathurin SOH, Celestin LELE This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6941500/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 10 You are reading this latest preprint version Abstract Short-term load forecasting (STLF) plays a pivotal role in power grid stability and economic dispatch, but conventional models often fail to address the dual challenges of data noise and complex spatiotemporal load dynamics. To bridge this gap, this paper presents FIRE-CNN-LSTM, an innovative hybrid computational intelligence model for short-term load forecasting that synergistically integrates fuzzy rough sets for uncertainty-aware data refinement, adaptive fuzzy membership functions for robust feature representation, and a Differential Evolution-optimized CNN-LSTM architecture for multi-scale temporal pattern learning. The proposed framework addresses critical challenges in power load forecasting by combining fuzzy logic's ability to handle data imprecision with deep learning's capacity for complex pattern recognition, further enhanced by evolutionary optimization of hyperparameters. Evaluated on real-world hourly load data from Malaysia, our model demonstrates superior performance with 60% RMSE reduction compared to conventional approaches, R2 > 0.999 prediction accuracy, and 22% improved generalization over non-fuzzy deep learning benchmarks. The work contributes to computational intelligence applications in energy systems by introducing a novel fuzzy-rough data preprocessing layer for noise resilience, developing an evolutionary-optimized hybrid neural architecture, and validating significant practical improvements in forecasting reliability that translate to 3-5% operational cost savings in grid management scenarios. Physical sciences/Mathematics and computing/Computer science Physical sciences/Mathematics and computing/Computational science Physical sciences/Mathematics and computing/Applied mathematics Fuzzy rough sets Differential Evolution CNN-LSTM Hybrid computational intelligence short-term load forecasting Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Under Review Version 1 posted Editorial decision: Revision requested 25 Aug, 2025 Reviews received at journal 23 Aug, 2025 Reviewers agreed at journal 22 Aug, 2025 Reviews received at journal 22 Aug, 2025 Reviewers agreed at journal 22 Aug, 2025 Reviewers invited by journal 26 Jun, 2025 Editor assigned by journal 26 Jun, 2025 Editor invited by journal 23 Jun, 2025 Submission checks completed at journal 23 Jun, 2025 First submitted to journal 20 Jun, 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-6941500","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":478103783,"identity":"5bba64bf-7f55-45b7-bcc2-ad959b6a1d49","order_by":0,"name":"Franck-Steve KAMDEM KENGNE","email":"data:image/png;base64,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","orcid":"","institution":"University of Dschang","correspondingAuthor":true,"prefix":"","firstName":"Franck-Steve","middleName":"KAMDEM","lastName":"KENGNE","suffix":""},{"id":478103784,"identity":"15c9452d-2437-4bd0-b6cf-5c4ea30172a5","order_by":1,"name":"Mathurin SOH","email":"","orcid":"","institution":"University of Dschang","correspondingAuthor":false,"prefix":"","firstName":"Mathurin","middleName":"","lastName":"SOH","suffix":""},{"id":478103785,"identity":"97c45388-d79b-424e-a304-fe40d146ca28","order_by":2,"name":"Celestin LELE","email":"","orcid":"","institution":"University of Dschang","correspondingAuthor":false,"prefix":"","firstName":"Celestin","middleName":"","lastName":"LELE","suffix":""}],"badges":[],"createdAt":"2025-06-20 20:53:15","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-6941500/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6941500/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":85715986,"identity":"3bb94ac6-767f-4edb-98d2-64f4f2f9280d","added_by":"auto","created_at":"2025-07-01 04:01:16","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1086666,"visible":true,"origin":"","legend":"","description":"","filename":"SNarticleFIRECNNLSTM.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6941500/v1_covered_9a23b804-c2f2-4e15-9ebb-2f7d720e04e8.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"FIRE-CNN-LSTM: A Fuzzy Rough Set-Evolved Hybrid Deep Learning Model for Short-Term Load Forecasting Using Computational Intelligence","fulltext":[],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":false,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":true,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":true,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Fuzzy rough sets, Differential Evolution, CNN-LSTM, Hybrid computational intelligence, short-term load forecasting","lastPublishedDoi":"10.21203/rs.3.rs-6941500/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6941500/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eShort-term load forecasting (STLF) plays a pivotal role in power grid stability and economic dispatch, but conventional models often fail to address the dual challenges of data noise and complex spatiotemporal load dynamics. To bridge this gap, this paper presents FIRE-CNN-LSTM, an innovative hybrid computational intelligence model for short-term load forecasting that synergistically integrates fuzzy rough sets for uncertainty-aware data refinement, adaptive fuzzy membership functions for robust feature representation, and a Differential Evolution-optimized CNN-LSTM architecture for multi-scale temporal pattern learning. The proposed framework addresses critical challenges in power load forecasting by combining fuzzy logic's ability to handle data imprecision with deep learning's capacity for complex pattern recognition, further enhanced by evolutionary optimization of hyperparameters. Evaluated on real-world hourly load data from Malaysia, our model demonstrates superior performance with 60% RMSE reduction compared to conventional approaches, R2 \u0026gt; 0.999 prediction accuracy, and 22% improved generalization over non-fuzzy deep learning benchmarks. 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