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

TL;DR

This paper demonstrates that incorporating wind data from neighboring stations significantly enhances short-term wind speed forecasts across various models, with improvements up to 20% in RMSE, outperforming other data sources and models.

Contribution

It systematically studies the benefits of using neighboring stations' data across multiple prediction methods, highlighting the advantage of non-linear models in wind forecasting.

Findings

Up to 20% RMSE improvement using neighboring station data.

Non-linear models outperform linear regression.

Spatiotemporal data from nearby stations is more beneficial than other information sources.

Abstract

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