A novel combination forecasting model for wind power integrating least square support vector machine, deep belief network, singular spectrum analysis and locality-sensitive hashing

Abstract 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 supportvector machine is applied to forecast the trend component while deep belief network is utilized to predict the fluctuation component. By this means, the advantages of the above two forecasting models 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 different kernel functions on the performance of least squares supportvector machine is investigated. The simulation results show that the prediction performance of the proposed combination model based on linear kernel function outperforms all the other comparison models from 1-step to 3-step forecasting. Therefore, it can be concluded that the proposed approach provides a promising and effective alternative for short-term wind power prediction.

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