ABSTRACT
Nonlinear wind fluctuations cause the deep learning to play an important role in the prediction of wind power. In this chapter, we propose common deep learning methods for the wind power forecasting. Recurrent neural network (RNN) and deep belief network (DBN) have applied to forecast wind power. The performance of deep learning approaches has compared on the basis of accuracy and efficiency. Mean absolute error and mean squared error methods have applied to measure the performances of the models. Our empirical studies have revealed that deep learning algorithms may produce significant and applicable results for the wind power forecasting.
