Successful integration of renewable energy sources like wind power into smart grids largely depends on accurate prediction of power from these intermittent sources. Production of wind power cannot be controlled as the wind speed can vary based on weather conditions. Accurate prediction of wind power can assist smart grid that intelligently decides on the usage of alternative power sources based on demand forecast. Time series wind speed data are normally used for wind power prediction. In this paper, we have investigated the usage of a set of secondary features obtained using deep learning for wind power prediction. Deep learning is a special form on neural network that is capable of capturing the structural properties of time series data in terms of a set of numeric features. More precisely, we have designed a two-stage autoencoder (a particular type of deep learning) and incorporated the structural features into a prediction framework. Using the structural features, we have achieved as high as 12.63% better prediction accuracy than traditionally used statistical features.

中文翻译：

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自动编码器用于风能预测

成功将风能等可再生能源整合到智能电网中，在很大程度上取决于对这些间歇性能源的准确预测。由于风速会根据天气条件而变化，因此无法控制风力发电。风力发电的准确预测可以帮助智能电网根据需求预测智能地决定替代电源的使用。时间序列风速数据通常用于风能预测。在本文中，我们研究了使用深度学习获得的一组次要特征对风电功率预测的使用。深度学习是神经网络的一种特殊形式，它能够根据一组数字特征来捕获时间序列数据的结构特性。更确切地说，我们设计了两阶段的自动编码器（一种特定类型的深度学习），并将结构特征整合到预测框架中。使用结构特征，我们的预测精度比传统使用的统计特征高出12.63％。