| 引用本文: | 欧阳庭辉,查晓明,秦 亮,熊 一,夏 添,黄鹤鸣.含核函数切换的风电功率短期预测新方法[J].电力自动化设备,2016,36(9): |
| OUYANG Tinghui,ZHA Xiaoming,QIN Liang,XIONG Yi,XIA Tian,HUANG Heming.Short-term wind power prediction based on kernel function switching[J].Electric Power Automation Equipment,2016,36(9): |
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| 摘要: |
| 为了降低大规模风电接入对电网造成的潜在威胁，提出基于核函数切换机制的混沌时间序列预测新方法，以进一步提高短期风电功率预测性能。首先，结合互信息法和虚假邻近点法实现原始风电功率序列的相空间重构，通过递归图和最大Lyapunov指数验证了风电功率是来自含确定性和随机性的混沌系统，说明了混沌预测方法的可行性。其次，给出了使用核函数进行混沌时间序列预测的实现方法，结合训练样本分析了该方法优于传统预测方法，并结合训练结果提出了使用支持向量机（SVM）训练最优核函数的切换机制，进一步提高了预测精度。最后，以美国BPA数据为实例，通过预测误差指标的对比分析，说明了含切换机制的核函数预测法可有效地实现风电功率短期预测，同时也证明了该方法可较好地提高风电预测性能。 |
| 关键词: 风电 预测 核函数 支持向量机 切换机制 混沌时间序列 风电功率预测 |
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| 基金项目:国家重点基础研究发展计划（973计划）资助项目（2012CB215101） |
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| Short-term wind power prediction based on kernel function switching |
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OUYANG Tinghui, ZHA Xiaoming, QIN Liang, XIONG Yi, XIA Tian, HUANG Heming
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School of Electrical Engineering，Wuhan University，Wuhan 430072，China
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| Abstract: |
| A method of chaotic time series prediction based on the switching regime of kernel functions is proposed to further improve the performance of short-term wind power prediction for reducing the potential risk of power grid caused by the large-scale wind power integration. The mutual information method and false nearest neighbor method are applied to reconstruct the phase-space of original wind power series. The recurrence plot and the maximum Lyapunov value are used to verify that，the wind power series are from a chaotic system with certainty and randomness and the chaotic prediction is applicable. The implementation of chaotic time series prediction based on kernel functions is given and the training sample analysis proves it is better than the traditional prediction method. According to the training results，the support vector machine is proposed to train the switching regime of optimal kernel functions for future improving the prediction accuracy. As an example，the comparison among the error indexes based on the data from BPA website proves that，the prediction based on the kernel functions with switching regime can effectively realize the short-term wind power prediction with better performances. |
| Key words: wind power prediction kernel functions support vector machines switching regime chaotic time series wind power prediction |
