power prediction concentrate on the use of multiple meteorological forecasts (from different meteorological offices) as input and forecast combination, as well as on the optimal use of spatially distributed measurement data for prediction error correction, or alternatively for issuing warnings on potentially large uncertainty. Google's DeepMind uses neural network to improve forecasting. Uncertainty of wind power forecasts Current designs are optimal only for nonturbulent, steady conditions. Design tools accounting for unsteadiness and turbulence are far less developed. Predictions of wind power output are traditionally provided in the form of point forecasts, i.e. a single value for each look-ahead time, which corresponds to the expectation or most-likely outcome. They have the advantage of being easily understandable because this single value is expected to tell everything about future power generation. Today, a major part of the research efforts on wind power forecasting still focuses on point prediction only, with the aim of assimilating more and more observations in the models or refining the resolution of physical models for better representing wind fields at the very local scale for instance. These efforts may lead to a significant decrease of the level of prediction error. However, even by better understanding and modeling both the meteorological and power conversion processes, there will always be an inherent and irreducible uncertainty in every prediction. This epistemic uncertainty corresponds to the incomplete knowledge one has of the processes that influence future events. Therefore, in complement to point forecasts of wind generation for the coming hours or days, of major importance is to provide means for assessing online the accuracy of these predictions. In practice today, uncertainty is expressed in the form of probabilistic forecasts or with risk indices provided along with the traditional point predictions. It has been shown that some decisions related to wind power management and trading are more optimal when accounting for prediction uncertainty. For the example of the trading application, studies have shown that reliable estimation of prediction uncertainty allows wind power producer to significantly increase their income in comparison to the sole use of an advanced point forecasting method. Other studies of this type deal with optimal dynamic quantification of reserve requirements, optimal operation of combined systems including wind, or multi-area multi-stage regulation. More and more research efforts are expected on prediction uncertainty and related topics. There are a number of questions that have still yet to be answered, according to a report