Classical Decomposition Time Series Predictive Model for the Forecast of Domestic Electric Energy Demand and Supply

Classical Decomposition Time Series Predictive Model for the Forecast of Domestic Electric Energy Demand and Supply | 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 Classical Decomposition Time Series Predictive Model for the Forecast of Domestic Electric Energy Demand and Supply Ruhiya Abubakar, Amevi Acakpovi Electrical/Electronics Engineering Accra Technical University Accra, Ghana [email protected] , and 2 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4889276/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 In modern technology and systems modeling, electric energy forecasting is extremely vital in gaining effective application of energy policies. This model is formulated after a thorough study of the power load conditions of Ghana as well as the factors that affect domestic electricity demand of supply in the Country was conducted. In Ghana, the LEAP (Long-range Energy Alternatives Planning) forecast model is officially applied for electricity demand and projection of power supply which comes with forecasting errors. Thus, there exists a crucial need to develop a forecasting model for the best energy policies formulation and consequent minimization of overall forecasting error compared to the LEAP model. Results from the quantitative classical multiplicative decomposition forecast model is comparatively precise with a reduced forecast error margin between − 5–4.5% compared to an existing prediction error margin viz., 1% to -11%. By virtue of the proposed study, accurate forecasting of power loads, improvement in utilization of electrical equipment, economies of scale and reduction in production cost can be attained. It is also essential to optimize power system resources for the attainment of energy conservation and overall reduction in emissions. High Energy and Particle Physics Time Series Forecast LEAP Model decomposition Domestic Power Demand 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-4889276","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":338338681,"identity":"94785d9e-471e-41d4-bba7-7ff7f07e99a6","order_by":0,"name":"Ruhiya Abubakar","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA8ElEQVRIiWNgGAWjYFACHgZmBoYEBgb2BqjAAQaQCDFaeA6AVZOiRSKBSC3m7WePSRfUpMmbz3x+8fPHNgY5vhsJzK8L8GiROZOXJj3jWI7hnNs5xRIH2xiMJW8ksFnPwKNFgiHHTJqHrYJxhnROGgNQS+IGoBZjHnxa+N8AtfyrsJ8heQaspZ6wFgmgLbxtOYkzJNiPgbQkGAD98hi/lnfJ1jP70pJn8OQwS5w5J2E488zDNmb8Dss9eLvgW7LtDPbjDz9UlNnI8x1PPvwZnxYkwGMAMgKIGdskiNPBwP4AxmL+QKSWUTAKRsEoGBkAALFkS+kUBWRUAAAAAElFTkSuQmCC","orcid":"https://orcid.org/0000-0002-3978-7598","institution":"Ghana Communicaton Technology University","correspondingAuthor":true,"prefix":"","firstName":"Ruhiya","middleName":"","lastName":"Abubakar","suffix":""},{"id":338338682,"identity":"72b4d22b-f184-408a-9fad-1afca18d8681","order_by":1,"name":"Amevi Acakpovi\t Electrical/Electronics Engineering Accra Technical University Accra, Ghana [email protected]","email":"","orcid":"","institution":"Accra Technical University","correspondingAuthor":false,"prefix":"","firstName":"Ghana","middleName":"[email protected] Amevi Acakpovi\t Electrical/Electronics Engineering Accra Technical University","lastName":"Accra","suffix":""},{"id":338338683,"identity":"3f27135e-00a0-4e7b-98be-abb032fa0f49","order_by":2,"name":"Micheal Agyare","email":"","orcid":"","institution":"Ghana Communication Technology University","correspondingAuthor":false,"prefix":"","firstName":"Micheal","middleName":"","lastName":"Agyare","suffix":""},{"id":338338684,"identity":"d414125a-572c-4818-8ffe-a1c4ba43d9f6","order_by":3,"name":"Samuel Afoakwa","email":"","orcid":"","institution":"Ghana Communicaton Technology University","correspondingAuthor":false,"prefix":"","firstName":"Samuel","middleName":"","lastName":"Afoakwa","suffix":""}],"badges":[],"createdAt":"2024-08-09 22:45:04","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-4889276/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4889276/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":62320519,"identity":"f18e3fe7-0d8b-48a6-9658-214e58082851","added_by":"auto","created_at":"2024-08-13 01:35:04","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":877660,"visible":true,"origin":"","legend":"","description":"","filename":"ClassicalDecompositionTimeSeriesPredictiveModelFINAL.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4889276/v1_covered_3294701f-19e8-4b72-b096-0bbcfc40fb22.pdf"}],"financialInterests":"The authors declare no competing interests.","formattedTitle":"\u003cp\u003eClassical Decomposition Time Series Predictive Model for the Forecast of Domestic Electric Energy Demand and Supply\u003c/p\u003e","fulltext":[],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":false,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"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":"Time Series, Forecast, LEAP Model, decomposition, Domestic Power Demand","lastPublishedDoi":"10.21203/rs.3.rs-4889276/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4889276/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eIn modern technology and systems modeling, electric energy forecasting is extremely vital in gaining effective application of energy policies. 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