Application of hybrid deep learning models for seasonal solar irradiance forecasting in the city of Pala.

Application of hybrid deep learning models for seasonal solar irradiance forecasting in the city of Pala. | 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 Application of hybrid deep learning models for seasonal solar irradiance forecasting in the city of Pala. OSEE MOUNKANG, Claude Vidal Aloyem Kaze, Dieudonné Nzoko Tayo, and 4 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8460615/v1 This work is licensed under a CC BY 4.0 License Status: Under Revision Version 1 posted 13 You are reading this latest preprint version Abstract Accurate prediction of solar irradiance is crucial for designing, controlling, and monitoring solar energy systems. However, its intermittent nature poses a significant challenge to achieving satisfactory forecasting results. This study aims to design new hybrid models and evaluate the performance of different forecasting models. The models combine Attentive Meteo-Embedding with long-term memory (AME-LSTM), Attentive Meteo-Embedding and convolutional neural networks (AME-CNN), Attentive Meteo-Embedding and gated recurrent units (AME-GRU) using the Adam optimization algorithm. The models take temperature, relative humidity, and wind direction as input variables for the years 2015–2023. The results show that the AME-GRU model performs exceptionally well in summer and spring, while the AME-CNN model excels in summer and autumn. However, the AME-LSTM model has notable limitations, with higher errors in winter. This study can contribute to the development of a hybrid power plant for the city of Pala. Forecasting solar irradiation hybrid models performance hybrid power plant Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Under Revision Version 1 posted Editorial decision: Revision requested 11 May, 2026 Reviews received at journal 30 Apr, 2026 Reviewers agreed at journal 21 Apr, 2026 Reviewers agreed at journal 30 Mar, 2026 Reviews received at journal 20 Mar, 2026 Reviewers agreed at journal 04 Mar, 2026 Reviewers agreed at journal 23 Feb, 2026 Reviews received at journal 12 Feb, 2026 Reviewers agreed at journal 05 Feb, 2026 Reviewers invited by journal 02 Feb, 2026 Editor assigned by journal 19 Jan, 2026 Submission checks completed at journal 16 Jan, 2026 First submitted to journal 16 Jan, 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. 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-8460615","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":584159446,"identity":"019f7a95-563d-4ae8-ad4f-be9446076db6","order_by":0,"name":"OSEE MOUNKANG","email":"data:image/png;base64,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","orcid":"","institution":"University of Yaoundé I","correspondingAuthor":true,"prefix":"","firstName":"OSEE","middleName":"","lastName":"MOUNKANG","suffix":""},{"id":584159447,"identity":"6895b012-2614-4d75-9782-d14c2d9c6761","order_by":1,"name":"Claude Vidal Aloyem Kaze","email":"","orcid":"","institution":"University of Dschang","correspondingAuthor":false,"prefix":"","firstName":"Claude","middleName":"Vidal Aloyem","lastName":"Kaze","suffix":""},{"id":584159448,"identity":"17b6a4f5-8322-4ee5-8f49-61bfd58c4d49","order_by":2,"name":"Dieudonné Nzoko Tayo","email":"","orcid":"","institution":"University of Yaoundé I","correspondingAuthor":false,"prefix":"","firstName":"Dieudonné","middleName":"Nzoko","lastName":"Tayo","suffix":""},{"id":584159459,"identity":"f374b5ef-895e-4c1e-912f-7538c6f25a74","order_by":3,"name":"Hervice Roméo Fogno Fotso","email":"","orcid":"","institution":"University of Yaoundé I","correspondingAuthor":false,"prefix":"","firstName":"Hervice","middleName":"Roméo Fogno","lastName":"Fotso","suffix":""},{"id":584159461,"identity":"2e0ddaa4-5f5b-4b07-889a-80692a15f2b0","order_by":4,"name":"Duclair Paul Edouard Pountounynyi","email":"","orcid":"","institution":"University of Yaoundé I","correspondingAuthor":false,"prefix":"","firstName":"Duclair","middleName":"Paul Edouard","lastName":"Pountounynyi","suffix":""},{"id":584159462,"identity":"ad02c947-72de-4be1-86a8-07f18a4fa648","order_by":5,"name":"Germaine Kenmoe Djuidje","email":"","orcid":"","institution":"University of Yaoundé I","correspondingAuthor":false,"prefix":"","firstName":"Germaine","middleName":"Kenmoe","lastName":"Djuidje","suffix":""},{"id":584159463,"identity":"0b6989e3-d4ef-435d-9542-05044f575503","order_by":6,"name":"René Tchinda","email":"","orcid":"","institution":"University of Dschang","correspondingAuthor":false,"prefix":"","firstName":"René","middleName":"","lastName":"Tchinda","suffix":""}],"badges":[],"createdAt":"2025-12-27 11:08:30","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-8460615/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-8460615/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":101781095,"identity":"8e16aea1-3f2c-440c-834e-e10cc1fed278","added_by":"auto","created_at":"2026-02-03 14:57:08","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1766045,"visible":true,"origin":"","legend":"","description":"","filename":"Manuscrit.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8460615/v1_covered_5c1204d9-d9fa-497d-bb92-41414d1ba490.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Application of hybrid deep learning models for seasonal solar irradiance forecasting in the city of Pala.","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":"discover-applied-sciences","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"","sideBox":"Learn more about [Discover Applied Sciences](https://link.springer.com/journal/42452)","snPcode":"42452","submissionUrl":"https://submission.springernature.com/new-submission/42452/3","title":"Discover Applied Sciences","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Discover Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Forecasting, solar irradiation, hybrid models, performance, hybrid power plant","lastPublishedDoi":"10.21203/rs.3.rs-8460615/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8460615/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eAccurate prediction of solar irradiance is crucial for designing, controlling, and monitoring solar energy systems. However, its intermittent nature poses a significant challenge to achieving satisfactory forecasting results. This study aims to design new hybrid models and evaluate the performance of different forecasting models. The models combine Attentive Meteo-Embedding with long-term memory (AME-LSTM), Attentive Meteo-Embedding and convolutional neural networks (AME-CNN), Attentive Meteo-Embedding and gated recurrent units (AME-GRU) using the Adam optimization algorithm. The models take temperature, relative humidity, and wind direction as input variables for the years 2015\u0026ndash;2023. The results show that the AME-GRU model performs exceptionally well in summer and spring, while the AME-CNN model excels in summer and autumn. However, the AME-LSTM model has notable limitations, with higher errors in winter. This study can contribute to the development of a hybrid power plant for the city of Pala.\u003c/p\u003e","manuscriptTitle":"Application of hybrid deep learning models for seasonal solar irradiance forecasting in the city of Pala.","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-02-03 14:54:46","doi":"10.21203/rs.3.rs-8460615/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2026-05-11T07:31:14+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-04-30T10:45:35+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"102302292565083492416388983112905322991","date":"2026-04-21T11:14:15+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"287050747172573494040606452059799379963","date":"2026-03-30T09:58:52+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-03-20T04:43:45+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"23124361178583891535296124232740021505","date":"2026-03-04T10:48:20+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"199225615932205656395176042578368702766","date":"2026-02-23T05:17:00+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-02-12T07:03:19+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"293547163388551325703351608186514949618","date":"2026-02-06T04:49:11+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2026-02-02T08:37:19+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2026-01-19T12:26:17+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2026-01-16T10:54:49+00:00","index":"","fulltext":""},{"type":"submitted","content":"Discover Applied Sciences","date":"2026-01-16T10:38:38+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"discover-applied-sciences","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"","sideBox":"Learn more about [Discover Applied Sciences](https://link.springer.com/journal/42452)","snPcode":"42452","submissionUrl":"https://submission.springernature.com/new-submission/42452/3","title":"Discover Applied Sciences","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Discover Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"0a2127f3-5b9d-4bc5-96bb-5c310cf5168e","owner":[],"postedDate":"February 3rd, 2026","published":true,"recentEditorialEvents":[{"type":"decision","content":"Revision requested","date":"2026-05-11T07:31:14+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-04-30T10:45:35+00:00","index":73,"fulltext":""}],"rejectedJournal":[],"revision":"","amendment":"","status":"in-revision","subjectAreas":[],"tags":[],"updatedAt":"2026-02-03T14:54:47+00:00","versionOfRecord":[],"versionCreatedAt":"2026-02-03 14:54:46","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8460615","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8460615","identity":"rs-8460615","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}