An analog ensemble for short-term probabilistic solar power forecast

TL;DR: In this paper , the authors used deep learning methods to obtain reliable and accurate probabilistic forecasts from single-member numerical weather predictions of integrated vapor transport (IVT) under atmospheric river (AR) conditions.

TL;DR: In this article, the authors developed a general data-driven strategy combining Geographic Information Systems and Machine Learning methods to map the large-scale energy potential for three very popular sources of decentralized energy systems: wind energy, geothermal energy and solar energy.

TL;DR: In this article , some revised versions of the analog ensemble (AnEn) algorithm to improve its performance when predicting rare events of solar power production are presented. But the authors focus on the verification of the AnEn's performance over the rare conditions outlining the improvements achieved with the new AnEn models.

TL;DR: In this article, the authors proposed two approaches to temporally extrapolate wind speed boundary condition and parametric uncertainty using a more convenient setup in which a mesoscale ensemble is run over a short-term period (1 year), and only a single model covers the desired longterm period.

TL;DR: In this article, a brief discussion is presented regarding the operating temperature of one-sun commercial grade silicon-based solar cells/modules and its effect upon the electrical performance of photovoltaic installations.

TL;DR: In this paper, a range of applications of the Regional Atmospheric Modeling System (RAMS), a comprehensive mesoscale meterological modeling system, are discussed, including large eddy simulations (LES) and simulations of thunderstorms, cumulus fields, mesoscales, convective systems, mid-latitude cirrus clouds, winter storms, mechanically and thermally-forced meso-scale systems, and mesoscopic atmospheric disperision.

TL;DR: In this article, the continuous ranked probability score (CRPS) is decomposed into a reliability part and a resolution/uncertainty part, in a way similar to the decomposition of the Brier score.

TL;DR: In this paper, the authors demonstrate that this common practice can lead to grossly misguided inferences, unless the scoring function and the forecasting task are carefully matched, and demonstrate that point forecasting methods are compared by means of an error measure or scoring function, with the absolute error and the squared error being key examples.