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In this paper, we address the issue of short-term wind speed prediction at agiven site. We show that, when one uses spatiotemporal information as providedby wind data of neighboring stations, one significantly improves the predictionquality. Our methodology does not focus on any peculiar forecasting model butrather considers a set of various prediction methods, from a very basic linearregression to different machine learning models. In each case, our approachconsists in specifically and incrementally studying the benefits of using winddata of the surrounding stations. We show that, at all horizons ranging from 1to 6 hours ahead, the relative gain on the RMSE of the predicted wind speed canincrease up to 20 %. For all the considered forecasting methods, we show thatsuch a gain is far better than the one obtained by considering other kind ofinformation like local weather variables or seeking for an optimal deeplearning model. Moreover we provide evidence that non-linear models, as neuralnetworks or gradient boosting methods, significantly outperform linearregression. These conclusions are simply interpreted as resulting from theability of a method to capture the transport of the information by the mainflow in the upwind direction.

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