Correlation based neuro-fuzzy Wiener type wind power forecasting model by using special separate signals

Abstract

The wind power system is characterized by complex structures, numerous effect elements, and complicated physical relationships, which bring great difficulties to establish the wind power forecasting model and result in the unsatisfactory prediction accuracy of wind power. In this paper, a correlation based neuro-fuzzy Wiener type wind power forecasting model is proposed by using special separate signals to decouple dynamic linear and static nonlinear characteristics of wind power systems for high-accuracy wind power forecasting. This method introduces an autoregressive exogenous model and fuzzy neural network to explore the underlying distribution characteristics of wind power systems by correlation analysis of the linear and nonlinear information contained in time-series data of the wind power system. In addition, in order to eliminate outliers in the original data set, an abnormal data cleaning approach combining quartile and two-stage clustering algorithm is employed to reduce the computation time and improve the model accuracy. Moreover, the convergence of the Wiener model is proved by the Lyapunov function. Simulation results verify the high accuracy and generalization ability of the proposed model both in the gale and breeze seasons.