工程设计学报2017，Vol.24Issue(2)：187-195,9.DOI:10.3785/j.issn.1006-754X.2017.02.010
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
Super-short-time wind power combination forecasting based on support vector machine optimized by EMD-SC and AGSA
摘要
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.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Key words
super-short-time wind power forecasting/empirical mode decomposition/spectral clustering/ameliorated gravitational search algorithm/support vector machine分类
机械制造引用本文复制引用
江岳春,杨旭琼,陈礼锋,贺飞..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[J].工程设计学报,2017,24(2):187-195,9.基金项目
国家自然科学基金资助项目(51277057) （51277057）
