Short-Term Solar Irradiance and Power Forecasting in Uncertain Photovoltaic Systems

Short-Term Solar Irradiance and Power Forecasting in Uncertain Photovoltaic Systems | 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 Solar Irradiance and Power Forecasting in Uncertain Photovoltaic Systems Manel Marweni, Mansour Hajji, Majdi Mansouri, Mohamed Faouzi Mimouni This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-5766097/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 Renewable energy (RE), while essential for the energy transition, poses significant challenges due to its variability and reliance on environmental factors , such as sunlight availability for Grid-Connected Photovoltaic (GCPV) systems. This intermittent nature complicates maintaining a stable balance between energy production and demand. To address these challenges, Energy Management (EM) plays a vital role in enabling optimal planning and resource allocation. In this context, forecasting becomes crucial for predicting production fluctuations and supporting proactive decision-making. Short-term forecasting, in particular, facilitates quick anticipation of variations and real-time adaptation of management strategies. This study proposes a Feedforward Neural Network (FFNN) model for short-term forecasting in renewable energy systems, effectively addressing variability challenges. By incorporating prediction intervals, the model ensures accurate predictions with quantified uncertainty. The model demonstrates high performance, with irradiance Mean Absolute Error (MAE) ranging between 0.3115 and 0.3506, Root Mean Squared Error (RMSE) between 0.4061 and 0.4802, and R 2 of 1.00. For power, the MAE ranges from 7.0136 to 7.8298, RMSE from 8.2715 to 9.2084, and R 2 of 0.99, underscoring its reliability for energy management applications. Renewable Energy Grid Connected Photovoltaic System Energy Management Forecasting Interval Analysis FeedForward Neural Network 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-5766097","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":398958529,"identity":"33a073aa-a50f-40c5-a5c2-b5fb525cc7f7","order_by":0,"name":"Manel Marweni","email":"","orcid":"","institution":"University of Monastir","correspondingAuthor":false,"prefix":"","firstName":"Manel","middleName":"","lastName":"Marweni","suffix":""},{"id":398958530,"identity":"a03548e7-5ee4-45a2-8c4f-618b34448433","order_by":1,"name":"Mansour Hajji","email":"","orcid":"","institution":"Higher Institute of Applied Sciences and Technology of Kasserine, Kairouan University","correspondingAuthor":false,"prefix":"","firstName":"Mansour","middleName":"","lastName":"Hajji","suffix":""},{"id":398958531,"identity":"c0a5cfc1-ed7f-4ac4-b6bd-d68cdcd8dcf1","order_by":2,"name":"Majdi Mansouri","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA2ElEQVRIiWNgGAWjYFADZuYDEMYB4rWwJZCqhYHHgDgtug3ciZ8L/tgkbm/n+fzhbRuDHN+NBMZHN/BoMTvAu1l6Zlta4pzDvNsk57YxGEveSGA2zsGvZYM0b8PhxBnMvNuYedsYEjfcSGCTJqBl82+ePyAtPI8/A7XUE6NlmzQPG1gLgzRQS4IBQS1AL1jztqUZz2BmM5Occ07CcOaZh834/XK8d/Ntnj82sjP4Dz/+8KbMRp7vePLBx/i0MDAjc3gYJIAkYwM+DWiAhwS1o2AUjIJRMHIAAFKQSTbDUVG5AAAAAElFTkSuQmCC","orcid":"","institution":"Sultan Qaboos University","correspondingAuthor":true,"prefix":"","firstName":"Majdi","middleName":"","lastName":"Mansouri","suffix":""},{"id":398958532,"identity":"44ca25ea-66b1-40ab-9df0-1ae14c5458ae","order_by":3,"name":"Mohamed Faouzi Mimouni","email":"","orcid":"","institution":"University of Monastir","correspondingAuthor":false,"prefix":"","firstName":"Mohamed","middleName":"Faouzi","lastName":"Mimouni","suffix":""}],"badges":[],"createdAt":"2025-01-05 05:08:03","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-5766097/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-5766097/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":75263738,"identity":"eaecf702-dcec-4fd3-967d-66c6d1eae32d","added_by":"auto","created_at":"2025-02-02 15:31:37","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":761756,"visible":true,"origin":"","legend":"","description":"","filename":"Manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-5766097/v1_covered_bf7d466d-6b30-4abf-8553-03199fab630d.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Short-Term Solar Irradiance and Power Forecasting in Uncertain Photovoltaic Systems","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":"Renewable Energy, Grid Connected Photovoltaic System, Energy Management, Forecasting, Interval Analysis, FeedForward Neural Network","lastPublishedDoi":"10.21203/rs.3.rs-5766097/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-5766097/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eRenewable energy (RE), while essential for the energy transition, poses significant challenges due to its variability and reliance on environmental factors , such as sunlight availability for Grid-Connected Photovoltaic (GCPV) systems. 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