Short term cloud nowcasting for a solar power plant based on irradiance historical Data
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...
| Autores: | , , , , |
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| Tipo de recurso: | artículo |
| Fecha de publicación: | 2018 |
| País: | España |
| Institución: | Universidad Complutense de Madrid (UCM) |
| Repositorio: | Docta Complutense |
| Idioma: | inglés |
| OAI Identifier: | oai:docta.ucm.es:20.500.14352/13009 |
| Acceso en línea: | https://hdl.handle.net/20.500.14352/13009 |
| Access Level: | acceso abierto |
| Palabra clave: | 004.8 Time-series Radiation Cloud nowcasting GHI LSTM Supervised machine learning Computer Science Artificial Intelligence Inteligencia artificial (Informática) 1203.04 Inteligencia Artificial |
| Sumario: | 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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