125789 20241125101135.0 doi 10.1016/j.solener.2023.03.007 sideral 133184 ART-2023-133184 eng Talayero, Ana P. (orcid)0000-0001-9823-4777 Machine Learning models for the estimation of the production of large utility-scale photovoltaic plants 2023 Access copy available to the general public Unrestricted Photovoltaic (PV) energy development has increased in the last years mainly based on large utility-scale plants. These plants are characterised by a huge number of panels connected to high-power inverters occupying a large land area. An accurate estimation of the power production of the PV plants is needed for failure detection, identifying production deviations, and the integration of the plants into the power grid. Various studies have used Machine Learning estimation techniques developed on very small PV plants. This paper deals with large utility-scale plants and uses all the available information to represent the non-uniform radiation over the whole studied solar field. Variables measured in up to four meteorological stations and distributed across the plant are used. Three PV plants with 1, 2 and 4 meteorological stations have been used to develop Machine Learning models. The hyperparameters were systematically optimised, demonstrating the improvements by comparing with a simple model based on Multiple Linear Regression. The best results were obtained with the Random Forest technique for the three PV plants, providing a RMS error value ranging from 1.9% to 5.4%. The final models were compared with those found in the literature for tiny PV plants showing in general much better performance. info:eu-repo/semantics/openAccess by-nc-nd http://creativecommons.org/licenses/by-nc-nd/3.0/es/ 6.0 2023 ENERGY & FUELS 54 / 171 = 0.316 2023 Q2 T1 1.311 2023 Renewable Energy, Sustainability and the Environment 2023 Q1 Materials Science (miscellaneous) 2023 Q1 13.9 2023 info:eu-repo/semantics/article info:eu-repo/semantics/publishedVersion Melero, Julio J. Universidad de Zaragoza (orcid)0000-0003-2360-0845 Llombart, Andrés (orcid)0000-0001-6350-4474 Yürüsen, Nurseda Y. (orcid)0000-0002-1206-9756 5009 535 Universidad de Zaragoza Dpto. Ingeniería Eléctrica Área Ingeniería Eléctrica 254 (2023), 88-101 Sol. energy Solar Energy 0038-092X 3751894 http://zaguan.unizar.es/record/125789/files/texto_completo.pdf Versión publicada 2741206 http://zaguan.unizar.es/record/125789/files/texto_completo.jpg?subformat=icon icon Versión publicada oai:zaguan.unizar.es:125789 articulos driver 2024-11-22-12:00:45 ARTICLE