论文标题
风能预测从现象到4小时的预测：一种具有可变选择的学习方法
Wind power predictions from nowcasts to 4-hour forecasts: a learning approach with variable selection
论文作者
论文摘要
我们研究风速和风力发电的短期预测（每10分钟至4小时）。这些数量的准确预测对于减轻风电场间歇性生产对能源系统和市场的负面影响至关重要。我们使用机器学习将数值天气预测模型中的输出与本地观测结合在一起。前者提供有关更高量表动态的有价值的信息，而后者则提供了更新鲜和特定于位置的数据。为了使结果适用于从业者，我们专注于可以处理大量数据的知名方法。我们使用线性技术和非线性技术研究了第一个变量选择。然后，我们利用这些结果来预测风速和风能，但重点是线性模型与非线性模型。为了进行风能预测，我们还比较了间接方法（通过功率曲线传递的风速预测）和间接方法（直接预测风能）。
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).
