We study short-term prediction of wind speed and wind power (every 10 minutes up to 4 hours ahead). Accurate forecasts for these quantities are crucial to mitigate the negative effects of wind farms' intermittent production on energy systems and markets. We use machine learning to combine outputs from numerical weather prediction models with local observations. The former provide valuable information on higher scales dynamics while the latter gives the model fresher and location-specific data. So as to make the results usable for practitioners, we focus on well-known methods which can handle a high volume of data. We study first variable selection using both a linear technique and a nonlinear one. Then we exploit these 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：我们研究风速和风力的短期预测(每10分钟至前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。我们利用机器学习将数字天气预测模型的产出与当地观测结合起来。前者提供关于更高尺度动态的宝贵信息,而后者则提供模型更新和具体位置的数据。为了让执行人员能够使用这些结果,我们侧重于已知的能够处理大量数据的方法。我们利用线性技术和非线性技术研究第一个变量选择。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,我们还比较了间接方法(风速预测通过电曲线传递)和间接方法(直接预测风力)。