论文标题
利用附近电台的数据来改善短期风速预测
Leveraging data from nearby stations to improve short-term wind speed forecasts
论文作者
论文摘要
在本文中，我们解决了给定站点上短期风速预测的问题。我们表明，当人们使用相邻站点的风数据提供的时空信息时，人们会显着提高预测质量。我们的方法不关注任何特殊的预测模型，而是考虑从非常基本的线性回归到不同的机器学习模型的各种预测方法。在每种情况下，我们的方法都包括并逐步研究使用周围电台的风数据的好处。我们表明，从前1到6个小时的视野中，预测风速的RMSE上的相对增益可以提高到20％。对于所有考虑的预测方法，我们表明，通过考虑当地天气变量等其他类型的信息或寻求最佳深度学习模型而获得的增益要好得多。此外，我们提供的证据表明，非线性模型作为神经网络或梯度增强方法，明显优于线性回归。这些结论简单地解释为方法是由于方法通过向上方向捕获主要流量的信息传输的能力。
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.
