Abstract With the rapid growth of wind power penetration into modern power grids, wind speed forecasting (WSF) becomes an increasing important task in the planning and operation of electric power and energy systems. However, WSF is quite challengeable due to its highly varying and complex features. In this paper, a novel hybrid deep neural network forecasting method is constituted. A feature selection method based on mutual information is developed in the WSF problem. With the real-time big data from the wind farm running log, the deep neural network model for WSF is established using a stacked denoising auto-encoder and long short-term memory network. The effectiveness of the deep neural network is evaluated by 10-minutes-ahead WSF. Comparing with the traditional multi-layer perceptron network, conventional long short-term memory network and stacked auto-encoder, the resulting deep neural network significantly improves the forecasting accuracy.

中文翻译：

---

基于特征选择的深度神经网络风速预测

摘要 随着风电在现代电网中的快速渗透，风速预测（WSF）成为电力和能源系统规划和运行中越来越重要的任务。然而，WSF 由于其高度变化和复杂的特性而极具挑战性。本文构建了一种新的混合深度神经网络预测方法。在WSF问题中开发了一种基于互信息的特征选择方法。利用风电场运行日志的实时大数据，利用堆叠去噪自编码器和长短期记忆网络建立WSF深度神经网络模型。深度神经网络的有效性由提前 10 分钟的 WSF 评估。与传统的多层感知器网络相比，