Mathieu David

TL;DR: In this article, the authors present an in-depth review of the current methods used to forecast solar irradiance in order to facilitate selection of the appropriate forecast method according to needs.

TL;DR: In this article, the authors present detailed information about solar resource data and the resulting data products needed for each stage of a solar energy project, from initial site selection to systems operations.

TL;DR: It is found that for hour ahead solar forecasting, the machine learning techniques slightly improve the performances exhibited by the linear AR and the scaled persistence model, however, the improvement appears to be more pronounced in case of unstable sky conditions.

TL;DR: This paper assesses the performances of a commonly used combination of two linear models (ARMA and GARCH) in econometrics in order to provide probabilistic forecasts of solar irradiance and the proposed model is able to perform point forecasts as accurately as other methods based on machine learning techniques.

TL;DR: To analyze and compare solar forecasts, the well-established Murphy–Winkler framework for distribution-oriented forecast verification is recommended as a standard practice and the use of the root mean square error (RMSE) skill score based on the optimal convex combination of climatology and persistence methods is highly recommended.