Renewable Energy Forecasting using Optimized Quantum Temporal Model based on Ninja Optimization Algorithm

Renewable Energy Forecasting using Optimized Quantum Temporal Model based on Ninja Optimization Algorithm | 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 Renewable Energy Forecasting using Optimized Quantum Temporal Model based on Ninja Optimization Algorithm Mona Ahmed Yassen, El-Sayed M. El-kenawy, Mohamed Gamal Abdel-Fattah, and 2 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-5943520/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 27 Apr, 2025 Read the published version in Scientific Reports → Version 1 posted 8 You are reading this latest preprint version Abstract Artificial intelligence allows improvements in renewable energy systems by increasing efficiency while enhancing reliability and reducing costs. Renewable energy forecasting receives substantial improvement by applying deep learning methods as one of its promising approaches. The research utilizes QTM with NiOA optimization for achieving maximum forecasting performance. NiOA functions through critical optimization processes when enhancing deep learning models with high accuracy for large complex datasets by selecting the most appropriate features. Fundamental data preparation steps, including normalization scaling, and gap handling, play a vital role before using input data for reliable renewable energy forecasting operations. Using the Ninja binary optimization engine produces superior results than all tested binary algorithms, including SBO, bSCA, bFA, bGA, bFEP, bGSA, bDE, bTSH and bBA, resulting in enhanced classification accuracy. The superior capability of bNinja to choose optimal features establishes its usefulness for renewable energy forecasting applications. Experimental implementation revealed that incorporating the Ninja Optimization Algorithm with the QTM model delivered the best R² performance at 95.15 % with an exceptional RMSE value of 0.00003, thus establishing its ability to optimize renewable energy forecasting accuracy. Physical sciences/Energy science and technology Biological sciences/Computational biology and bioinformatics/Machine learning Full Text Additional Declarations No competing interests reported. Supplementary Files RenewableEnergyForecastingusingOptimizedQuantumTemporalModelbasedonNinjaOptimizationAlgorithmFinalSubmission.zip Cite Share Download PDF Status: Published Journal Publication published 27 Apr, 2025 Read the published version in Scientific Reports → Version 1 posted Editorial decision: Accepted 02 Apr, 2025 Reviews received at journal 01 Apr, 2025 Reviews received at journal 24 Mar, 2025 Reviewers agreed at journal 24 Mar, 2025 Reviewers agreed at journal 23 Mar, 2025 Reviewers invited by journal 23 Mar, 2025 Submission checks completed at journal 22 Mar, 2025 First submitted to journal 17 Mar, 2025 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. 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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-5943520","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":433008115,"identity":"6c6046f3-68eb-4940-a811-e53e032d436f","order_by":0,"name":"Mona Ahmed Yassen","email":"data:image/png;base64,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","orcid":"","institution":"Department of Electronics and Communications Engineering, Faculty of Engineering","correspondingAuthor":true,"prefix":"","firstName":"Mona","middleName":"Ahmed","lastName":"Yassen","suffix":""},{"id":433008116,"identity":"de06e551-9939-44c1-a82a-8ca3a1293c9c","order_by":1,"name":"El-Sayed M. 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