Accelerated Uncertainty Quantification of Photovoltaic Solar Cell Using Data-Driven Surrogate Modeling: A Comparative Study

Accelerated Uncertainty Quantification of Photovoltaic Solar Cell Using Data-Driven Surrogate Modeling: A Comparative Study | 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 Accelerated Uncertainty Quantification of Photovoltaic Solar Cell Using Data-Driven Surrogate Modeling: A Comparative Study Amir Parnianifard, Sushank Chaudhary, Abhishek Sharma, Sunita Khichar, and 1 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7407903/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 Accurate Uncertainty Quantification (UQ) is essential for the design and performance prediction of photovoltaic devices, where fabrication tolerances can significantly affect electrical characteristics. This study applies data-driven surrogate modeling to accelerate UQ in silicon solar cells, modeled using the single-diode equivalent circuit with series and shunt resistances. Four fabrication-related parameters—series resistance, ideality factor, cell area, and shunt resistance—are treated as random variables with prescribed probability distributions to capture manufacturing variability. Monte Carlo simulation serves as the benchmark for statistical assessment. Four established surrogate techniques—ordinary kriging, radial basis function networks, support vector machines, and ordinary polynomial regression—are evaluated alongside a Modified Polynomial Regression (MPR) method, previously proposed by the first author, which integrates transformation-based feature engineering and optimized parameter selection. Surrogates are trained using Latin hypercube sampling with a limited set of high-fidelity simulations and tested on large-scale Gaussian and uniform samples. While Kriging can achieve slightly higher point accuracy in some cases, its performance varies more across repeated runs. In contrast, MPR delivers competitive accuracy with greater robustness, achieving the highest overall ranking based on signal-to-noise metrics, enabling faster and more reliable UQ in solar energy applications. Solar Cell Modeling Uncertainty Quantification Monte Carlo Simulation Surrogate Modeling Modified Polynomial Regression Photovoltaic Performance Prediction 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. 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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-7407903","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":504294978,"identity":"271337e2-8c2f-47ef-8082-056020029558","order_by":0,"name":"Amir Parnianifard","email":"data:image/png;base64,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","orcid":"","institution":"University of Glasgow","correspondingAuthor":true,"prefix":"","firstName":"Amir","middleName":"","lastName":"Parnianifard","suffix":""},{"id":504294979,"identity":"aaa3394c-497d-447a-b734-4bb9c0485752","order_by":1,"name":"Sushank Chaudhary","email":"","orcid":"","institution":"Guangdong University of Petrochemical Technology","correspondingAuthor":false,"prefix":"","firstName":"Sushank","middleName":"","lastName":"Chaudhary","suffix":""},{"id":504294980,"identity":"71191fb3-dbcd-4732-bf29-0a57dea89085","order_by":2,"name":"Abhishek Sharma","email":"","orcid":"","institution":"National Institute of Technology Hamirpur","correspondingAuthor":false,"prefix":"","firstName":"Abhishek","middleName":"","lastName":"Sharma","suffix":""},{"id":504294981,"identity":"1c708af6-261c-4bd2-86b5-7eebd853cadf","order_by":3,"name":"Sunita Khichar","email":"","orcid":"","institution":"Chulalongkorn University","correspondingAuthor":false,"prefix":"","firstName":"Sunita","middleName":"","lastName":"Khichar","suffix":""},{"id":504294982,"identity":"e276c57b-f29b-464a-a799-0d81b796ee23","order_by":4,"name":"Muhammad Ali Imran","email":"","orcid":"","institution":"University of Glasgow","correspondingAuthor":false,"prefix":"","firstName":"Muhammad","middleName":"Ali","lastName":"Imran","suffix":""}],"badges":[],"createdAt":"2025-08-19 11:08:17","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-7407903/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-7407903/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":93231035,"identity":"2530017e-f805-4411-8f9e-558c2b47d27b","added_by":"auto","created_at":"2025-10-10 13:08:42","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2977236,"visible":true,"origin":"","legend":"","description":"","filename":"ManuscriptParnianifardetalAcceleratedUQPhotovoltaic.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7407903/v1_covered_20072deb-ce08-4f60-b6f0-456bd9473ada.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Accelerated Uncertainty Quantification of Photovoltaic Solar Cell Using Data-Driven Surrogate Modeling: A Comparative Study","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":"Solar Cell Modeling, Uncertainty Quantification, Monte Carlo Simulation, Surrogate Modeling, Modified Polynomial Regression, Photovoltaic Performance Prediction","lastPublishedDoi":"10.21203/rs.3.rs-7407903/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7407903/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"Accurate Uncertainty Quantification (UQ) is essential for the design and performance prediction of photovoltaic devices, where fabrication tolerances can significantly affect electrical characteristics. 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