Leveraging data from nearby stations to improve short-term wind speed forecasts

In this paper, we address the issue of short-term wind speed prediction at a given site. We show that, when one uses spatiotemporal information as provided by data neighboring stations, significantly improves quality. Our methodology does not focus on any peculiar forecasting model but rather considers set various methods, from very basic linear regression to different machine learning models. each case, our approach consists in specifically and incrementally studying benefits using surrounding stations. all horizons ranging 1 6 h ahead, relative gain RMSE predicted can increase up 20 %. For considered that such is far better than obtained considering other kind like local weather variables or seeking for an optimal deep model. Moreover provide evidence non-linear models, neural networks gradient boosting outperform regression. These conclusions are simply interpreted resulting ability method capture transport main flow upwind direction.