We study the prediction of short term wind speed and wind power (every 10 minutes up to 4 hours ahead). Accurate forecasts for those quantities are crucial to mitigate the negative effects of wind farms' intermittent production on energy systems and markets. For those time scales, outputs of numerical weather prediction models are usually overlooked even though they should provide valuable information on higher scales dynamics. In this work, we combine those outputs with local observations using machine learning. So as to make the results usable for practitioners, we focus on simple and well known methods which can handle a high volume of data. We study first variable selection through two simple techniques, a linear one and a nonlinear one. Then we exploit those results to forecast wind speed and wind power still with an emphasis on linear models versus nonlinear ones. For the wind power prediction, we also compare the indirect approach (wind speed predictions passed through a power curve) and the indirect one (directly predict wind power).

翻译：我们研究短期风速和风力的预测(每隔10分钟至4小时)。准确预测这些数量对于减轻风力农场间歇性生产对能源系统和市场的负面影响至关重要。对于这些时间尺度,数字天气预测模型的产出通常被忽视,尽管它们应该提供关于更高尺度动态的宝贵信息。在这项工作中,我们利用机器学习将这些产出与当地观测结果结合起来。为了让实践者使用这些结果,我们侧重于简单和众所周知的方法,这些方法能够处理大量数据。我们通过两种简单技术,一种是线性技术,另一种是非线性技术,研究首选变量。然后我们利用这些结果预测风速和风力,同时强调线性模型和非线性模型。关于风力预测,我们还比较间接方法(风速预测通过电曲线传递)和间接方法(直接预测风力)。