Comparative Energy Consumption Forecasting Using XGBoost, LightGBM,LSTM ,and ARIMAX with IoT-Based Data Acquisition

Comparative Energy Consumption Forecasting Using XGBoost, LightGBM,LSTM ,and ARIMAX with IoT-Based Data Acquisition | 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 Comparative Energy Consumption Forecasting Using XGBoost, LightGBM,LSTM ,and ARIMAX with IoT-Based Data Acquisition TIKAOUI HICHAM This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7547316/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 This study presents a comparative analysis of various artificial intelligence techniques to develop an efficient Building Energy Management System (BEMS) capable of predicting energy consumption and contributing to the reduction of energy waste. The research focuses on identifying the most appropriate method for building a predictive model of energy consumption, using both machine learning and statistical approaches. Several methods are evaluated to determine the optimal model for this task, comparing their performance to highlight the strengths and limitations of each one. This work specifically focuses on the comparative analysis of XGBoost, LightGBM,LSTM, and ARIMAX, complemented with an IoT-based data acquisition system designed to capture energy consumption patterns in Moroccan homes. The results demonstrate the superiority of machine learning models, particularly LightGBM, over traditional statistical approaches in terms of accuracy, execution speed, and ability to handle the inherent complexity of energy consumption data Artificial intelligence Iot Machine Learning LSTM LightGBM Energy management Full Text Additional Declarations The authors declare no competing interests. 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-7547316","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":510986249,"identity":"36e1611a-8656-4513-a4bb-4954514b800a","order_by":0,"name":"TIKAOUI HICHAM","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAABFElEQVRIiWNgGAWjYBACA2YGNjCDjQfMtSFeiwRUSxoDD0EtDFAtUKWHCWsxZ2d+9uBnDkMdH8/hh495Cs7L2UsksEn8qGCQ52/gTvyARYtlM5u5Ye82oMN424yNeQxuG/MAtUj2nGEwnHGAd7MENocd5mGT4AVp4WcwkwZqSewB2cLbxsC4gYF3Ay4tkn/BWti/AbWcqwdpkfz7j8EeqGXzDxxapMG28PaAbDmQAHKYNG8DQyJQyzbstrCZSctuk5Bs4zlTbDjHINmw58zDZmuZYxLJMw7zbrPApuX84WeSb7fZ8Mv3pG988OaPnTx7e/LBm29qbGz723s338Ad1igOYGyAiDDjVj8KRsEoGAWjAD8AABKUUGzFY3RMAAAAAElFTkSuQmCC","orcid":"","institution":"FST ELHOUCEIMA","correspondingAuthor":true,"prefix":"","firstName":"TIKAOUI","middleName":"","lastName":"HICHAM","suffix":""}],"badges":[],"createdAt":"2025-09-05 22:33:46","currentVersionCode":1,"declarations":{"humanSubjects":false,"vertebrateSubjects":false,"conflictsOfInterestStatement":false,"humanSubjectEthicalGuidelines":false,"humanSubjectConsent":false,"humanSubjectClinicalTrial":false,"humanSubjectCaseReport":false,"vertebrateSubjectEthicalGuidelines":false},"doi":"10.21203/rs.3.rs-7547316/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-7547316/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":90854440,"identity":"8c3146f9-0ba9-42a8-b920-fb329de43473","added_by":"auto","created_at":"2025-09-09 04:09:58","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":593098,"visible":true,"origin":"","legend":"","description":"","filename":"Manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7547316/v1_covered_686339a9-edf3-47f2-bedf-04744bb0c9d2.pdf"}],"financialInterests":"The authors declare no competing interests.","formattedTitle":"\u003cp\u003eComparative Energy Consumption Forecasting Using XGBoost,\u003c/p\u003e\n\u003cp\u003eLightGBM,LSTM ,and ARIMAX with IoT-Based Data Acquisition\u003c/p\u003e","fulltext":[],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":false,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":true,"highlight":"","institution":"Abdelmalek Essaâdi University","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 intelligence, Iot, Machine Learning, LSTM, LightGBM, Energy management","lastPublishedDoi":"10.21203/rs.3.rs-7547316/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7547316/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eThis study presents a comparative analysis of various artificial intelligence techniques to develop an efficient Building Energy Management System (BEMS) capable of predicting energy consumption and contributing to the reduction of energy waste. 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