RT Journal Article
T1 Photovoltaic power electricity generation nowcasting combining sky camera images and learning supervised algorithms in the Southern Spain
A1 Trigo González, Mauricio
A1 Cortés Carmona, Marcelo
A1 Marzo, Aitor
A1 Alonso-Montesinos, Joaquín
A1 Martínez Durbán, Mercedes
A1 López Rodríguez, Gabriel
A1 Portillo, Carlos
A1 Batlles, Francisco J.
AB The alternation between cloudy and clear skies alters the photovoltaic production. This makes it necessary toanticipate these disturbances hours in advance for the correct operation of the electricity distribution plants andnetworks. In this paper, two short-term forecasting models (3 h) are developed to forecast the photovoltaicproduction in an integrated plant in the CIESOL building of the University of Almería. The methodology used isbased on sky camera images and Artificial Intelligence techniques. Two models have been developed andcompared applying artificial neural network (ANN) and support vector machine (SVM) techniques. The globalirradiance predicted using sky camera images is used as an input variable in both models. In addition, theoperational status of the plants has been included as an input parameter through the performance ratio. Theresults have shown that the errors made by ANN and SVM are very similar. For all sky conditions, the uncertaintyof the production forecast differs by less than 2% from the uncertainty of the solar resource, which is the mainsource of error in the production models developed.
PB Elsevier
SN 0960-1481
SN 1879-0682 (electrónico)
YR 2023
FD 2023
LK https://hdl.handle.net/10272/22166
UL https://hdl.handle.net/10272/22166
LA eng
NO Trigo-González, M., Cortés-Carmona, M., Marzo, A., Alonso-Montesinos, J., Martínez-Durbán, M., López, G., Portillo, C., & Batlles, F. J. (2023). Photovoltaic power electricity generation nowcasting combining sky camera images and learning supervised algorithms in the Southern Spain. In Renewable Energy (Vol. 206, pp. 251-262). Elsevier BV. https://doi.org/10.1016/j.renene.2023.01.111
NO The authors acknowledge the generous financial support provided by CONICYT under the project  ANID/FONDAP/15110019 SERC-Chile. Also, the authors want to acknowledge the project MAPV Spain, withreference PID2020-118239RJ-I00, financed by Ministerio de Ciencia e Innovación, and co-financed by the European Regional Development Fund. Finally, authors also acknowledge the Consejería de Transformación Económica, Industria, Conocimiento y Universidades de la Junta de Andalucía within the framework of the FEDER of Andalusia 2014-2020 Project Reference UHU-202031. A. Marzo thanks for the Ramon y Cajal contract (RYC2021-031958-I), funded by the Spanish Ministerio de Ciencia e Innovación MCIN/AEI/10.13039/501100011033 and by the European Union "NextGenerationEU/PRTR. Funding for open access charge: Universidad de Granada / CBUA
DS Repositorio Institucional de la Universidad de Huelva
RD 1 jun 2026