Resumen

GUTIERREZ-COREA, Federico Vladimir; MANSO-CALLEJO, Miguel Ángel  y  SERRADILLA-GARCIA, Francisco. Space-time short-term solar radiation modeling and forecasting through artificial neural networks and geostatistics. Rev. cartogr. [online]. 2020, n.100, pp.13-40.  Epub 14-Mar-2022. ISSN 2663-3981.  https://doi.org/10.35424/rcarto.i100.699.

The enrichment of knowledge about solar irradiance (SI) on the Earth’s surface and its prediction (forecast) has a great interest in renewable energy (RE), such as systems based on solar energy (SE) and for different applications industrial and environmental. At the present research it has been investigated five techniques of spatial estimation of the SI in 15 minutes of temporal resolutions for the Spanish mainland, with several spatial configurations. It’s been found that the Geostatistics through Regression Kriging, using auxiliary variables -one of this: the SI estimated from Satellite Images- allows spatially estimates the SI beyond the 25 km identified by the related research as the maximum distance limit to the estimation point. It has been experimented with the Artificial Neural Networks (ANN) modelling for the short-term forecasting of the SI, using close observations (spatial component) as part of its inputs, and the results are promising. In this way the error levels diminish, regarding to the related researches, under the following conditions: when the temporal horizons of the forecast is lower or equal to 3 hours, the neighbors stations to be included as input to the models should be at a 55 km of maximum distance.

Palabras llave : artificial neural networks; geostatistics; forecast..

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