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Título:
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Short term cloud nowcasting for a solar power plant based on irradiance historical Data
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Autores:
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Caballero Roldán, Rafael ;
Zarzalejo Tirado, Luis Fernando ;
Otero Martín, Álvaro ;
Piñuel Moreno, Luis ;
Wilbert, Stefan
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Tipo de documento:
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texto impreso
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Editorial:
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Universidad Nacional de La Plata, 2018-12
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Dimensiones:
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application/pdf
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Nota general:
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cc_by_nc
info:eu-repo/semantics/openAccess
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Idiomas:
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Palabras clave:
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Estado = Publicado
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Materia = Ciencias: Informática: Inteligencia artificial
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Tipo = Artículo
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Resumen:
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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 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.
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En línea:
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https://eprints.ucm.es/50740/1/Pi%C3%B1uel%2001%20libre%2BCC%28by-sa%29.pdf
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