Wind power prediction is important for the smart grid safe operation and scheduling, and it can improve the economic and technical\npenetration of wind energy.The intermittent and the randomness of wind would affect the accuracy of prediction. According to the\nsequence correlation between wind speed and wind power data, we propose a method for short-term wind power prediction.The\nproposed method adopts the wind speed in every sliding data window to obtain the continuous prediction of wind power. Then, the\nnonlinear partial least square is adopted to map the wind speed under the time series to wind power.The model carries the neural\nnetwork as the nonlinear function to describe the inner relation, and the outputs of hidden layer nodes are the extension term of\nthe original independent input matrix to partial least squares regression. To verify the effectiveness of the proposed algorithm, the\nreal data of wind power with different working conditions are adopted in experiments. The proposed method, backpropagation\nneural network, radial basis function neural network, support vector machine, and partial least square are performed on the real\ndata and their effectiveness is compared. The experimental results show that the proposed algorithm has higher precision, and the\nreal power running curves also verify that the proposed method can predict the wind power in short-term effectively.
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