Wind speed forecasting with correlation network pruning and augmentation: A two-phase deep learning method

Abstract

To ensure the operational reliability of power systems, it is important for wind speed signal forecasting systems of wind turbines to be efficient, accurate and stable. This paper proposes a two-phase deep learning structure with network augmentation and pruning. By introducing the cross-correlation and quasi-convex optimization, a fractional quadratic programming problem and related convex optimization models are constructed to generate the augmented data for this proposed internal network; by pruning weakly correlated convolution channels, the redundant features of its external network are reduced. Furthermore, the closed-form solution of the convex optimization model is derived, which reduces the computational complexity considerably from O(n*log(2N)) to O(n). The proposed approach has been extensively validated using the real data of the wind farm in China. The results of the numerical experiments demonstrate that the proposed method achieves the superior performance in the training flexibility, model accuracy, stability, and interpretability.