LAUSR

As a source of clean and pollution-free renewable energy, wind power has attracted much attention and has been increasingly integrated into the existing power system. However, the uncertain and volatile wind speed makes the utilization of wind power a challenging task. Hence, it is essential to design an accurate forecast method to deal with the uncertainty caused by wind speed. This paper proposes a multiobjective interval prediction method based on wavelet neural network (WNN) for short-term wind speed forecast. This method can generate a set of Pareto optimal solutions which represents a set of prediction models that can directly construct the prediction intervals. An advanced multiobjective evolutionary algorithm, preference inspired co-evolutionary algorithm using goal vectors, is investigated to train the WNN model. Two case studies are carried out with real wind speed data of Victoria and Edmonton in Canada to justify the effectiveness of the proposed method. The numerical results also show the superiority of the proposed forecast approach compared with some benchmark methods.