Deep learning-based forecasting of aggregated CSP production

Abstract

This paper introduces deep learning-based forecasting models for the continuous prediction of the aggregated production generated by CSP plants in Spain. These models use as inputs the expected top of atmosphere irradiance values and available weather conditions forecasts for the locations where the main CSP power plants are installed. The performances of the forecast models are analysed and compared by means of the most extended metrics in the literature for a whole year of CSP energy production.

A deep learning model for intra-day forecasting of solar irradiance using satellite-based estimations in the vicinity of a PV power plant

“…Therefore, days in zone III are the hardest to predict, and days in the zone CI are the easiest, as they are clear sky days with low variability. All the GHI measurements used in this work are available at (Segarra-Tamarit et al, 2020) to facilitate the reproducibility of this research. Furthermore, as the archive of GHI estimates at http://msgcpp.knmi.nl only stores the last three years of data, the matrices used in this work are also provided within this repository.…”