@misc{10481/80168,
year = {2023},
month = {1},
url = {https://hdl.handle.net/10481/80168},
abstract = {The alternation between cloudy and clear skies alters the photovoltaic production. This makes it necessary to anticipate these disturbances hours in advance for the correct operation of the electricity distribution plants and networks. In this paper, two short-term forecasting models (3 h) are developed to forecast the photovoltaic production in an integrated plant in the CIESOL building of the University of Almería. The methodology used is based on sky camera images and Artificial Intelligence techniques. Two models have been developed and compared applying artificial neural network (ANN) and support vector machine (SVM) techniques. The global irradiance predicted using sky camera images is used as an input variable in both models. In addition, the operational status of the plants has been included as an input parameter through the performance ratio. The results have shown that the errors made by ANN and SVM are very similar. For all sky conditions, the uncertainty of the production forecast differs by less than 2% from the uncertainty of the solar resource, which is the main source of error in the production models developed.},
publisher = {Elsevier},
keywords = {Photovoltaic plant},
keywords = {Nowcasting},
keywords = {Sky cameras},
keywords = {Machine learning},
keywords = {Solar resource assessment},
title = {Photovoltaic power electricity generation nowcasting combining sky camera images and learning supervised algorithms in the Southern Spain},
doi = {https://doi.org/10.1016/j.renene.2023.01.111},
author = {Trigo-González, Mauricio and Cortés-Carmona, Marcelo and Marzo, Aitor and Alonso- Montesinos, Joaquín and Martínez-Durbán, Mercedes and López, Gabriel and Portillo, Carlos and Batlles, Francisco Javier},
}