000125789 001__ 125789
000125789 005__ 20240731103330.0
000125789 0247_ $$2doi$$a10.1016/j.solener.2023.03.007
000125789 0248_ $$2sideral$$a133184
000125789 037__ $$aART-2023-133184
000125789 041__ $$aeng
000125789 100__ $$0(orcid)0000-0001-9823-4777$$aTalayero, Ana P.
000125789 245__ $$aMachine Learning models for the estimation of the production of large utility-scale photovoltaic plants
000125789 260__ $$c2023
000125789 5060_ $$aAccess copy available to the general public$$fUnrestricted
000125789 5203_ $$aPhotovoltaic (PV) energy development has increased in the last years mainly based on large utility-scale plants. These plants are characterised by a huge number of panels connected to high-power inverters occupying a large land area. An accurate estimation of the power production of the PV plants is needed for failure detection, identifying production deviations, and the integration of the plants into the power grid. Various studies have used Machine Learning estimation techniques developed on very small PV plants. This paper deals with large utility-scale plants and uses all the available information to represent the non-uniform radiation over the whole studied solar field. Variables measured in up to four meteorological stations and distributed across the plant are used. Three PV plants with 1, 2 and 4 meteorological stations have been used to develop Machine Learning models. The hyperparameters were systematically optimised, demonstrating the improvements by comparing with a simple model based on Multiple Linear Regression. The best results were obtained with the Random Forest technique for the three PV plants, providing a RMS error value ranging from 1.9% to 5.4%. The final models were compared with those found in the literature for tiny PV plants showing in general much better performance.
000125789 540__ $$9info:eu-repo/semantics/openAccess$$aby-nc-nd$$uhttp://creativecommons.org/licenses/by-nc-nd/3.0/es/
000125789 590__ $$a6.0$$b2023
000125789 592__ $$a1.311$$b2023
000125789 591__ $$aENERGY & FUELS$$b54 / 170 = 0.318$$c2023$$dQ2$$eT1
000125789 593__ $$aRenewable Energy, Sustainability and the Environment$$c2023$$dQ1
000125789 593__ $$aMaterials Science (miscellaneous)$$c2023$$dQ1
000125789 594__ $$a13.9$$b2023
000125789 655_4 $$ainfo:eu-repo/semantics/article$$vinfo:eu-repo/semantics/publishedVersion
000125789 700__ $$0(orcid)0000-0003-2360-0845$$aMelero, Julio J.$$uUniversidad de Zaragoza
000125789 700__ $$0(orcid)0000-0001-6350-4474$$aLlombart, Andrés
000125789 700__ $$0(orcid)0000-0002-1206-9756$$aYürüsen, Nurseda Y.
000125789 7102_ $$15009$$2535$$aUniversidad de Zaragoza$$bDpto. Ingeniería Eléctrica$$cÁrea Ingeniería Eléctrica
000125789 773__ $$g254 (2023), 88-101$$pSol. energy$$tSolar Energy$$x0038-092X
000125789 8564_ $$s3751894$$uhttps://zaguan.unizar.es/record/125789/files/texto_completo.pdf$$yVersión publicada
000125789 8564_ $$s2741206$$uhttps://zaguan.unizar.es/record/125789/files/texto_completo.jpg?subformat=icon$$xicon$$yVersión publicada
000125789 909CO $$ooai:zaguan.unizar.es:125789$$particulos$$pdriver
000125789 951__ $$a2024-07-31-09:46:25
000125789 980__ $$aARTICLE