Short Term Cloud Nowcasting for a Solar Power Plant based on Irradiance Historical Data

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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 part...

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Autores principales: Caballero, Rafael, Zarzalejo, Luis F., Otero, Álvaro, Piñuel, Luis, Wilbert, Stefan
Formato: Articulo
Lenguaje:Inglés
Publicado: 2018
Materias:
Ciencias Informáticas
cloud nowcasting
GHI
LSTM,
supervised machine learning
prendizaje automático supervisado
previsión de nubes
Acceso en línea:http://sedici.unlp.edu.ar/handle/10915/71620
http://journal.info.unlp.edu.ar/JCST/article/view/1112
Aporte de:
SEDICI (UNLP) de Universidad Nacional de La Plata
id I19-R120-10915-71620
record_format dspace
institution Universidad Nacional de La Plata
institution_str I-19
repository_str R-120
collection SEDICI (UNLP)
language Inglés
topic Ciencias Informáticas
cloud nowcasting
GHI
LSTM,
supervised machine learning
prendizaje automático supervisado
previsión de nubes
spellingShingle Ciencias Informáticas
cloud nowcasting
GHI
LSTM,
supervised machine learning
prendizaje automático supervisado
previsión de nubes
Caballero, Rafael
Zarzalejo, Luis F.
Otero, Álvaro
Piñuel, Luis
Wilbert, Stefan
Short Term Cloud Nowcasting for a Solar Power Plant based on Irradiance Historical Data
topic_facet Ciencias Informáticas
cloud nowcasting
GHI
LSTM,
supervised machine learning
prendizaje automático supervisado
previsión de nubes
description 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.
format Articulo
Articulo
author Caballero, Rafael
Zarzalejo, Luis F.
Otero, Álvaro
Piñuel, Luis
Wilbert, Stefan
author_facet Caballero, Rafael
Zarzalejo, Luis F.
Otero, Álvaro
Piñuel, Luis
Wilbert, Stefan
author_sort Caballero, Rafael
title Short Term Cloud Nowcasting for a Solar Power Plant based on Irradiance Historical Data
title_short Short Term Cloud Nowcasting for a Solar Power Plant based on Irradiance Historical Data
title_full Short Term Cloud Nowcasting for a Solar Power Plant based on Irradiance Historical Data
title_fullStr Short Term Cloud Nowcasting for a Solar Power Plant based on Irradiance Historical Data
title_full_unstemmed Short Term Cloud Nowcasting for a Solar Power Plant based on Irradiance Historical Data
title_sort short term cloud nowcasting for a solar power plant based on irradiance historical data
publishDate 2018
url http://sedici.unlp.edu.ar/handle/10915/71620
http://journal.info.unlp.edu.ar/JCST/article/view/1112
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