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| Super-short-time wind power combination forecasting based on support vector machine optimized by EMD-SC and AGSA |
| JIANG Yue-chun, YANG Xu-qiong, CHEN Li-feng, HE Fei |
| College of Electrical and Information Engineering, Hunan University, Changsha 410082, China |
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Abstract Due to the randomness, volatility and relativity of the wind power, it brings great challenges to wind power integration. To improve the ultra short-term prediction accuracy of the wind power, a kind of method for predicting super-short-term wind power based on empirical mode decomposition (EMD) and spectral clustering (SC) and ameliorated gravitational search algorithm (AGSA) that could optimize the learning parameters of support vector machine (SVM) was put forward. Firstly, the raw data of the wind power was denoised by EMD to eliminate the irregular data; then the cluster analysis of the subsequences from EMD was carried out by SC, and SVM's model was optimized by applying AGSA to predict each subsequence respectively; finally the results of the subsequences were added together to get the ultimate predicted value. Taking one wind farm's actual data as an example, the simulation indicates that the proposed model can improve the accuracy and veracity when predicting wind power. Meanwhile, it also suggests the reasonability of this method. The method can forecast wind power accurately.
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Published: 28 April 2017
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基于EMD-SC和AGSA优化支持向量机的超短期风电功率组合预测
风电功率存在较大的随机性、波动性和相关性，这会对风电并网带来极大的挑战。为提高超短期风电功率预测精度，提出一种基于经验模态分解（empirical mode decomposition，EMD）、谱聚类（spectral clustering，SC）和改进型引力搜索算法（ameliorated gravitational search algorithm，AGSA）优化支持向量机（support vector machine，SVM）参数的超短期风电功率组合预测方法。首先通过经验模态分解对风电原始数据进行去噪处理，剔除不规则的数据；然后应用谱聚类对经验模态分解后的子序列进行聚类分析，再应用改进型引力搜索算法优化支持向量机模型对各个子序列进行预测；最后将各子序列的预测结果相加得到最终预测值。以某风电场的实际数据为算例，仿真研究表明，所提出的组合模型能够提高风电功率预测精度，且预测效果较好，同时也证明了所采用方法的合理性。该方法能够用于风电功率的精确预测。
关键词：
超短期风电功率预测,
经验模态分解,
谱聚类,
改进型引力搜索算法,
支持向量机
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