Flux Density Distribution Forecasting in Concentrated Solar Tower Plants: A Data-Driven Approach

Flux Density Distribution Forecasting in Concentrated Solar Tower Plants: A Data-Driven Approach | 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 Flux Density Distribution Forecasting in Concentrated Solar Tower Plants: A Data-Driven Approach Mathias Kuhl, Max Pargmann, Mehdi Cherti, Jenia Jitsev, Daniel Maldonado Quinto, and 1 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4223216/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 Concentrated Solar Power (CSP) systems, particularly those employing heliostat fields combined with a central tower, demonstrate substantial capacity for producing dispatchable, sustainable energy and fuel. This is achieved by focusing the sunlight with up to thousands of individual heliostats onto a single receiver. Forecasting the focal spot of each heliostat at any solar position becomes imperative to ensure optimal control. Nevertheless, the existing cutting-edge techniques aimed at predicting this flux density distribution either suffer from inaccuracies or entail substantial costs. In response to these challenges, our study introduces a novel approach involving a generative model that learns the shape and intensity patterns of the focal spots directly from images captured of the calibration target. We developed a purely data-driven methodology to generate the focal spots of the heliostats corresponding to various sun positions. The model is based on the StyleGAN architecture with adapted learnable input vectors for each individual heliostat and sun positions as input condition. The methodology’s effectiveness is demonstrated through training and evaluation on data collected from a research power plant, where it achieved a flux prediction accuracy of 89% on the calibration target surface. Our work offers a novel solution for predicting flux density distributions in solar power plants in a fully data-driven way with a neural network. Notably, this method achieves cost efficiency by utilizing data obtained during standard operational procedures. Impressively, this method attains accuracy levels comparable to or exceeding those of current state-of-the-art techniques. Energy Engineering Solar power tower Flux Density Prediction Camera-Target Method Heliostat Machine Learning Full Text Additional Declarations The authors declare no competing interests. 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. 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-4223216","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":287943001,"identity":"b70a91ec-0196-46cc-a630-6765e1a1fda7","order_by":0,"name":"Mathias 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