In-Depth Analysis of a Solar Panel Performance: Efficiency and Productivity Methods Examination Based on Numerical Model and Emotional Artificial Neural Network

In-Depth Analysis of a Solar Panel Performance: Efficiency and Productivity Methods Examination Based on Numerical Model and Emotional Artificial Neural Network | 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 In-Depth Analysis of a Solar Panel Performance: Efficiency and Productivity Methods Examination Based on Numerical Model and Emotional Artificial Neural Network Ali Basem, Serikzhan Opakhai, Zakaria Mohamed Salem Elbarbary, and 2 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4241855/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 02 Jan, 2025 Read the published version in Scientific Reports → Version 1 posted 11 You are reading this latest preprint version Abstract This article presents an analysis and evaluation of the performance of a standard 200 W solar cell, with a particular emphasis on the energy and exergy aspects of the cell. A numerical model and a novel machine-learning model (Emotional Artificial Neural Network) were employed to simulate and ascertain the electrical characteristics of the system, encompassing the open-circuit voltage, short-circuit current, system resistances, maximum power point characteristics, and characteristic curves. A novel approach has yielded mathematical equations capable of calculating efficiency levels. The system's operational and electrical parameters, along with environmental conditions such as solar radiation, wind speed, and ambient temperature, were empirically observed and documented during a day. A comparative analysis was conducted to validate the model by comparing its results with the data provided by the manufacturer and the data gathered through experimental means. During the duration of the trial, spanning from 7:00 to 17:00, the results indicate that the energy efficiency rate exhibited variations within a range of 10.34 to 14.00 percent. The average energy efficiency assessed throughout this time period was found to be 13.6 percent. During the duration of the experiment, the degree of exergy efficiency exhibited variability, ranging from 13.57 to 16.41 percent, with an average value of 15.70 percent. Furthermore, the results of the EANN model indicate that the suggested method to forecasting energy, exergy, and power is feasible for simulating problems at a reduced computational expense compared to the numerical model. Physical sciences/Engineering/Electrical and electronic engineering Physical sciences/Engineering/Mechanical engineering Solar Cell Comparative Analysis Environmental Conditions Energy and ExergyEfficiency Machine-learning Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Published Journal Publication published 02 Jan, 2025 Read the published version in Scientific Reports → Version 1 posted Editorial decision: Revision requested 09 Jul, 2024 Reviews received at journal 07 Jul, 2024 Reviewers agreed at journal 27 Jun, 2024 Reviews received at journal 14 Jun, 2024 Reviewers agreed at journal 20 May, 2024 Reviewers agreed at journal 20 May, 2024 Reviewers invited by journal 20 May, 2024 Editor assigned by journal 20 May, 2024 Editor invited by journal 23 Apr, 2024 Submission checks completed at journal 23 Apr, 2024 First submitted to journal 09 Apr, 2024 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-4241855","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":294734705,"identity":"1ceaa3b3-7f75-465f-a9c4-f44bf4542155","order_by":0,"name":"Ali Basem","email":"","orcid":"","institution":"Warith Al-Anbiyaa University","correspondingAuthor":false,"prefix":"","firstName":"Ali","middleName":"","lastName":"Basem","suffix":""},{"id":294734707,"identity":"33ce8f64-250b-4656-b611-d755b58ebae8","order_by":1,"name":"Serikzhan Opakhai","email":"","orcid":"","institution":"L.N. 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