RT Journal Article
T1 Short term cloud nowcasting for a solar power plant based on irradiance historical Data
A1 Caballero Roldán, Rafael
A1 Zarzalejo Tirado, Luis Fernando
A1 Otero Martín, Álvaro
A1 Piñuel Moreno, Luis
A1 Wilbert, Stefan
AB 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.
PB Universidad Nacional de La Plata
SN 1666-6046
YR 2018
FD 2018-12
LK https://hdl.handle.net/20.500.14352/13009
UL https://hdl.handle.net/20.500.14352/13009
LA eng
NO © 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.
NO Ministerio de Economía y Competitividad (MINECO)
NO Universidad Complutense de Madrid/Banco de Santander
DS Docta Complutense
RD 12 may 2025