Application of Adaptive Neuro-Fuzzy Inference for Output Power Estimation in Photovoltaic Cells

Application of Adaptive Neuro-Fuzzy Inference for Output Power Estimation in Photovoltaic Cells | 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 Adaptive Neuro-Fuzzy Inference for Output Power Estimation in Photovoltaic Cells Hector Felipe Mateo-Romero, Mario Eduardo Carbonò de la Rosa, and 7 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7072185/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 21 Mar, 2026 Read the published version in Soft Computing → Version 1 posted 5 You are reading this latest preprint version Abstract This manuscript introduces two Adaptive Neuro-Fuzzy Inference Systems developed to predict the energy output of Photovoltaic cells. These models are trained using Electroluminescence imagery of the cells for input data along their Current-Voltage curves, which offer insights into the cells’ power output. The input characteristics of the cells are quantified based on the pixel distribution and categorized into three distinct classes: Black, White, and Gray values. By synergizing the rulebased interpretability of Fuzzy Logic with the adaptive learning prowess of Artificial Neural Networks, ANFIS is found as a superior approach for addressing this issue. Comparative analysis with other Machine Learning techniques demonstrates the ANFIS models’ enhanced performance, achieving a Mean Absolute Error (MAE) of 0.053 and a Mean Squared Error (MSE) of 0.007. Fuzzy Logic Photovoltaic Electroluminescence Machine Learning Full Text Cite Share Download PDF Status: Published Journal Publication published 21 Mar, 2026 Read the published version in Soft Computing → Version 1 posted Editorial decision: Major Revision 26 Aug, 2025 Reviewers agreed at journal 18 Jul, 2025 Reviewers invited by journal 17 Jul, 2025 Editor invited by journal 12 Jul, 2025 First submitted to journal 08 Jul, 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. 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-7072185","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":486853136,"identity":"b0f6c11b-3040-4fa2-9c92-84e08540ff1e","order_by":0,"name":"Hector Felipe Mateo-Romero","email":"","orcid":"","institution":"Universidad Autonoma de Madrid","correspondingAuthor":false,"prefix":"","firstName":"Hector","middleName":"Felipe","lastName":"Mateo-Romero","suffix":""},{"id":486853137,"identity":"21a9c570-7611-4e18-849a-9d8fa515b540","order_by":1,"name":"Mario Eduardo Carbonò de la Rosa","email":"","orcid":"","institution":"Universidad 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