Short-Term Solar Irradiance Forecasting Using Deep Learning Models and Agentic RNN-LSTM for Localized Energy Decisions

Short-Term Solar Irradiance Forecasting Using Deep Learning Models and Agentic RNN-LSTM for Localized Energy Decisions | 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 Short-Term Solar Irradiance Forecasting Using Deep Learning Models and Agentic RNN-LSTM for Localized Energy Decisions Jasia Jabeen, Nayab Asim, Irum Matloob, Muazzam Ali Khan Khattak, and 1 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-9087052/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 9 You are reading this latest preprint version Abstract Accurate forecasting of solar irradiance is critical to supporting the stability of the power grid and allowing for efficient use of renewable energy resources. As solar generation is highly dependent upon weather; therefore, accurate forecasting of short-term solar totals, or Global Horizontal Irradiance (GHI), is useful to energy planners in order to manage power supply and minimize uncertainties around actual operations. This research examines short-term GHI prediction by utilizing high-resolution, ground-based meteorological observations collected from 9 locations throughout Pakistan. The first modelling approach employs three deep learning architectures Long Short-Term Memory (LSTM), Gated Recurrent Unit (GRU), and Temporal Convolutional Networks (TCN) to capture temporal dependencies in multi-station weather data. The dataset covers the period 2014–2017 with 10-minute resolution, making it suitable for fine-grained short-term forecasting. A comprehensive preprocessing pipeline is applied, including outlier handling, feature engineering, cyclical temporal encoding, and robust scaling. Time-series sequences of 24 time steps are used as input to predict the next-step irradiance value. In addition to centralized multi-station modelling, a second experimental setup explores a multi-agent inspired forecasting framework for single-station prediction. In this configuration, a forecasting agent uses recurrent models (RNN and LSTM) to predict the next-hour irradiance, while a secondary decision agent uses these predictions within a rule-based energy management simulation to evaluate potential battery charging and grid-usage actions. Experimental results show that the optimized stacked LSTM model achieves the best performance for multi-station forecasting, with an R 2 score of 0.9907 and lower MAE and RMSE compared with GRU and TCN models. For the single-station setup, the agent-based LSTM demonstrates stable forecasting performance under localized weather dynamics. Comparative analysis with recent studies highlights the effectiveness of recurrent deep learning models for short-term solar irradiance forecasting. The proposed framework can assist utility operators and energy planners in improving solar resource management in regions with high solar potential. Earth and environmental sciences/Climate sciences Physical sciences/Energy science and technology Physical sciences/Engineering Physical sciences/Mathematics and computing Solar irradiance forecasting Global Horizontal Irradiance (GHI) Deep learning RNN LSTM GRU TCN networks Time-series prediction Multi-agent systems Renewable energy forecasting Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Under Review Version 1 posted Reviewers agreed at journal 30 Apr, 2026 Reviews received at journal 24 Apr, 2026 Reviewers agreed at journal 11 Apr, 2026 Reviewers agreed at journal 07 Apr, 2026 Reviewers invited by journal 02 Apr, 2026 Editor invited by journal 18 Mar, 2026 Editor assigned by journal 12 Mar, 2026 Submission checks completed at journal 12 Mar, 2026 First submitted to journal 10 Mar, 2026 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-9087052","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":619093289,"identity":"7b5842bd-5291-4ef4-a83a-9bed99aea1f8","order_by":0,"name":"Jasia Jabeen","email":"","orcid":"","institution":"Fatima Jinnah Women University","correspondingAuthor":false,"prefix":"","firstName":"Jasia","middleName":"","lastName":"Jabeen","suffix":""},{"id":619093290,"identity":"9f7ba4f7-e68d-42c9-96ee-46e5f3b173a4","order_by":1,"name":"Nayab Asim","email":"","orcid":"","institution":"Fatima Jinnah Women University","correspondingAuthor":false,"prefix":"","firstName":"Nayab","middleName":"","lastName":"Asim","suffix":""},{"id":619093291,"identity":"d1e477a5-49d1-4d44-be73-575abd20f615","order_by":2,"name":"Irum Matloob","email":"","orcid":"","institution":"Fatima Jinnah Women University","correspondingAuthor":false,"prefix":"","firstName":"Irum","middleName":"","lastName":"Matloob","suffix":""},{"id":619093292,"identity":"11c0d3ce-a590-42ca-9c57-1224f8f246fa","order_by":3,"name":"Muazzam Ali Khan Khattak","email":"","orcid":"","institution":"Harbin Institute of Technology","correspondingAuthor":false,"prefix":"","firstName":"Muazzam","middleName":"Ali Khan","lastName":"Khattak","suffix":""},{"id":619093293,"identity":"10c1851e-c597-428e-bddb-1723e42724eb","order_by":4,"name":"Huihui Song","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA0ElEQVRIiWNgGAWjYJACZsYGmwQ2BgY2EJtoLWkJbGwkajmcwEC0Fvn23sOvC3ecz+OT7zF7wFBhndjAfvYAXi0GZ86lWc88c7uYjY3H3IDhTHpiA09eAn4tEjlmxrxttxPb2Hi3STC2HU5skOAxwO+wGWAt56Ba/hGhheFGjvFj3rYDUC0NRGgxOHPGjJm3LRmoJf+bRMKxdOM2nhwCDmvvMf7M22aXOL/5WJrEhxpr2X72MwQcBox0CTgzgQGSBggB5g9EKBoFo2AUjIKRDADeOkCGKAR5RAAAAABJRU5ErkJggg==","orcid":"","institution":"Harbin Institute of Technology","correspondingAuthor":true,"prefix":"","firstName":"Huihui","middleName":"","lastName":"Song","suffix":""}],"badges":[],"createdAt":"2026-03-10 18:38:13","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-9087052/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-9087052/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":106724507,"identity":"fcdf0619-d2bd-4901-bb52-126236d2a6c3","added_by":"auto","created_at":"2026-04-12 18:28:20","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1091716,"visible":true,"origin":"","legend":"","description":"","filename":"TemplateforsubmissionstoScientificReports.pdf","url":"https://assets-eu.researchsquare.com/files/rs-9087052/v1_covered_ab06c7d0-3632-4bad-b558-0c6842b5fac5.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Short-Term Solar Irradiance Forecasting Using Deep Learning Models and Agentic RNN-LSTM for Localized Energy Decisions","fulltext":[],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":false,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"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":"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":"Solar irradiance forecasting, Global Horizontal Irradiance (GHI), Deep learning, RNN, LSTM, GRU, TCN networks, Time-series prediction, Multi-agent systems, Renewable energy forecasting","lastPublishedDoi":"10.21203/rs.3.rs-9087052/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-9087052/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"Accurate forecasting of solar irradiance is critical to supporting the stability of the power grid and allowing for efficient use of renewable energy resources. 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Comparative analysis with recent studies highlights the effectiveness of recurrent deep learning models for short-term solar irradiance forecasting. 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