基于改进经验模态分解和支持向量机的短期风速组合预测
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1.山东理工大学;2.国网山东省电力公司电力科学研究院

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TM614

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国网山东省电力公司2019年科技攻关项目基金(520626190050)


Short-term Wind Speed Combination Prediction Based on Improved Empirical Mode Decomposition and Support Vector Machine
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Shandong university of technology

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    摘要:

    为更精确地进行风速预测,提出一种利用带自适应噪声的完全集成经验模态分解方法(complete ensemble empirical mode decomposition with adaptive noise,CEEMDAN)和蝙蝠算法(bat algorithm,BA)优化支持向量机(support vector machine,SVM)的组合短期风速预测方法。首先用CEEMDAN对原始风速时间序列进行分解,得到一系列不同频率的子序列;其次,使用BA-SVM组合模型预测对分解后的各个子序列分别进行预测;最后,将各子序列的预测结果叠加得到风速预测值。仿真结果表明,该模型提高了预测精度,减小了误差。

    Abstract:

    In order to predict the wind speed more accurately, based on the complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) and bat algorithm (BA) to optimize the support vector machine, a combined model was proposed for short-term wind speed forecasting. Firstly, CEEMDAN was used to decompose the original wind speed time series into a series of subsequences with different frequencies. Secondly, the decomposed subsequences were forecasted by combined model of BA-SVM. Finally, the wind speed forecasting results was achieved by superposing each predicted subsequence. The simulation results suggest that the model improves the prediction accuracy and reduces the error.

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韩世浩,孙树敏,程 艳,等. 基于改进经验模态分解和支持向量机的短期风速组合预测[J]. 科学技术与工程, 2019, 19(36): 172-178.
HAN Shi-hao,,CHEN Yan, et al. Short-term Wind Speed Combination Prediction Based on Improved Empirical Mode Decomposition and Support Vector Machine[J]. Science Technology and Engineering,2019,19(36):172-178.

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历史
  • 收稿日期:2019-05-28
  • 最后修改日期:2019-07-12
  • 录用日期:2019-07-28
  • 在线发布日期: 2020-01-21
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