An RBF neural network combined with OLS algorithm and genetic algorithm for short-term wind power forecasting (Q2375733)

Summary: An accurate forecasting method for power generation of the wind energy conversion system (WECS) is urgently needed under the relevant issues associated with the high penetration of wind power in electricity systems. This paper proposes a hybrid method that combines the orthogonal least squares (OLS) algorithm and genetic algorithm (GA) to construct the radial basis function (RBF) neural network for short-term wind power forecasting. The RBF neural network is composed of three-layer structures, which contain the input, hidden, and output layers. The OLS algorithm is used to determine the optimal number of nodes in a hidden layer of the RBF neural network. With an appropriate RBF neural network structure, the GA is then used to tune the parameters in the network, including the centers and widths of the RBF and the connection weights in a second stage. To demonstrate the effectiveness of the proposed method, the method is tested on the practical information of wind power generation of a WECS installed at the Taichung coast of Taiwan. Comparisons of forecasting performance are made by the persistence method and back propagation neural networks. The good agreement between realistic values and forecasting values are obtained; the test results show that the proposed forecasting method is accurate and reliable.