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Accurate wind power prediction can alleviate the negative influence on power system caused by the integration of wind farms into grid. In this paper, a novel combination model is proposed with the purpose of enhancing short‐term wind power prediction precision. Singular spectrum analysis is utilized to decompose the original wind power series into the trend component and the fluctuation component. Then least squares support vector machine (LSSVM) is applied to forecast the trend component while deep belief network (DBN) is utilized to predict the fluctuation component. By this means, the performance advantages of LSSVM and DBN can be brought into full play. Moreover, the locality‐sensitive hashing search algorithm is introduced to cluster the nearest training samples to further improve forecasting accuracy. Besides, the effect of LSSVM based on different kernel functions and the number of the nearest …