Wind Energy Conversion System Power Forecasting Using Radial Basis Function Neural Network

An accurate forecasting method for wind power generation of the wind energy conversion system (WECS) can help the power system’s operator to reduce the risk of unreliability of electricity supply. This paper proposed a radial basis function (RBF) neural network method to forecast the wind power generation of WECS. To demonstrate the effectiveness of the proposed method, the method is tested on the practical information of wind power generation of a WECS. The good agreements between the realistic values and forecasting values are obtained; the numerical results show that the proposed forecasting method is accurate and reliable.

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