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    ASIA unversity > 管理學院 > 經營管理學系  > 期刊論文 >  Item 310904400/112033

    Please use this identifier to cite or link to this item: http://asiair.asia.edu.tw/ir/handle/310904400/112033

    Title: Short-Term Wind Power Prediction Based on Improved Chicken Algorithm Optimization Support Vector Machine
    Authors: Chao Fu;Guo-Quan Li;Kuo-Ping Lin;Hui-Juan Zhang
    Contributors: 經營管理學系
    Date: 2019-01
    Issue Date: 2019-09-10 15:08:02 (UTC+8)
    Abstract: Renewable energy technologies are essential contributors to sustainable energy including renewable energy sources. Wind energy is one of the important renewable energy resources. Therefore, efficient and consistent utilization of wind energy has been an important issue. The wind speed has the characteristics of intermittence and instability. If the wind power is directly connected to the grid, it will impact the voltage and frequency of the power system. Short-term wind power prediction can reduce the impact of wind power on the power grid and the stability of power system operation is guaranteed. In this study, the improved chicken swarm algorithm optimization support vector machine (ICSO-SVM) model is proposed to predict the wind power. The traditional chicken swarm optimization algorithm (CSO) easily falls into a local optimum when solving high-dimensional problems due to its own characteristics. So the CSO algorithm is improved and the ICSO algorithm is developed. In order to verify the validity of the ICSO-SVM model, the following work has been done. (1) The particle swarm optimization (PSO), ICSO, CSO and differential evolution algorithm (DE) are tested respectively by four standard testing functions, and the results are compared. (2) The ICSO-SVM and CSO-SVM models are tested respectively by two sets of wind power data. This study draws the following conclusions: (1) the PSO, CSO, DE and ICSO algorithms are tested by the four standard test functions and the test data are analyzed. By comparing it with the other three optimization algorithms, the ICSO algorithm has the best convergence effect. (2) The number of training samples has an obvious impact on the prediction results. The average relative error percentage and root mean square error (RMSE) values of the ICSO model are smaller than those of CSO-SVM model. Therefore, the ICSO-SVM model can efficiently provide credible short-term predictions for wind power forecasting.
    Relation: Sustainability
    Appears in Collections:[經營管理學系 ] 期刊論文

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