Abstract
In a context of sustainable development, interest for Concentrating Solar Power (CSP) is growing rapidly. One of the most challenging topics is to improve solar resource assessment and forecasting in order to optimize power plant operation. Indeed, in CSP plants, electricity generation is directly impacted by both availability and variability of the solar resource and, more specifically, by Direct Normal Irradiance (DNI). Moreover, in the framework of the CSPIMP research project, PROMES-CNRS has developped a sky imager able to provide High Dymanic Range (HDR) images. As a result, the present paper deals with the short-term forecasting of DNI using sky-imaging data. Preliminary results highlight that models (in particular based on artificial intelligence tools) that make use of the fractional cloud cover have the potential to outperform persistence models in terms of forecasting accuracy.
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