Caballero Roldán, RafaelZarzalejo Tirado, Luis FernandoOtero Martín, ÁlvaroPiñuel Moreno, LuisWilbert, Stefan2023-06-172023-06-172018-121666-604610.24215/16666038.18.e21https://hdl.handle.net/20.500.14352/13009© 2018 Universidad Nacional de La PLata This work has been partially supported by the Spanish MINECO project TIN2015-66471, and by the Santander-UCM project PR26/16-21B-1.This work considers the problem of forecasting the normal solar irradiance with high spatial and temporal resolution (5 minutes). The forecasting is based on a dataset registered during one year from the high resolution radiometric network at a operational solar power plan at Almeria, Spain. In particular, we show a technique for forecasting the irradiance in the next few minutes from the irradiance values obtained on the previous hour. Our proposal employs a type of recurrent neural network known as LSTM, which can learn complex patterns and that has proven its usability for forecasting temporal series. The results show a reasonable improvement with respect to other prediction methods typically employed in the studies of temporal series.engAtribución-NoComercial 3.0 Españahttps://creativecommons.org/licenses/by-nc/3.0/es/journal articlehttp://dx.doi.org/10.24215/16666038.18.e21http://journal.info.unlp.edu.aropen access004.8Time-seriesRadiationCloud nowcastingGHILSTMSupervised machine learningComputer ScienceArtificial IntelligenceInteligencia artificial (Informática)1203.04 Inteligencia Artificial