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Short-term wind speed forecasting based on improved ant colony algorithm for LSSVM

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Abstract

In this paper, a least squares support vector machine (LSSVM) model with parameter optimization is proposed for solving the problem that the forecast accuracy of neural network model and support vector machine model is not desirable for the sake of improving short-term wind speed forecast accuracy further. The parameters of LSSVM are optimized by the improved ant colony algorithm. Firstly, the parameters of LSSVM are regarded as the position vector of ants. Another argument is that the global search is carried out by selecting some ants randomly from the ant colony to guide the whole ant colony, while searching the optimal ant neighborhood. Furthermore, the optimal parameters of the model are obtained, and the wind speed prediction model of LSSVM is established through parameter optimization. Taking a wind farm in North China as an example, the collected wind speed data were taken in predicted experience, besides the results were compared with the BP neural network model and the LSSVM model. The results show that this model has significant advantages compared with the other two models and has high practical significance.

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Authors and Affiliations

  1. College of Control Science and Engineering of Hebei University of Technology, Tianjin, China

    Yanan Li, Peng Yang & Huajun Wang

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  1. Yanan Li
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  2. Peng Yang
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  3. Huajun Wang
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Correspondence to Yanan Li.

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Li, Y., Yang, P. & Wang, H. Short-term wind speed forecasting based on improved ant colony algorithm for LSSVM. Cluster Comput 22 (Suppl 5), 11575–11581 (2019). https://doi.org/10.1007/s10586-017-1422-2

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  • DOI: https://doi.org/10.1007/s10586-017-1422-2

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