湖南大学学报（自然科学版）2016，Vol.43Issue(10)：70-78,9.
基于EEMD-IGSA-LSSVM的超短期风电功率预测∗
Super-short-Time Wind Power Forecasting Based on EEMD-IGSA-LSSVM
摘要
Abstract
In order to improve the prediction accuracy of the output power of the wind farm under the premise of ensuring safe operation,a combination of wind power forecasting model based on Ensemble Em-pirical Mode of Decomposition (EEMD),Improved Gravitational Search Algorithm (IGSA)and Least Squares Support Vector Machine (LSSVM)was established.Firstly,the wind power time series was de-composed into a series of subsequences with significant differences in complexity by using EEMD algo-rithm.Secondly, the decomposed subsequence was reconstructed by the phase space reconstruction (PSR),and then,an IGSA-LSSVM prediction model of each sub-sequence reconstructed was established respectively.In order to analyze the differences of LSSVM which sets up different kernel functions,eight kinds of kernel function LSSVM prediction models were established,and the IGSA algorithm was adopted to solve those models.Finally,taking a wind farm in Inner Mongolia of China as an example,the simula-tion and calculation results illustrate that LSSVM prediction model based on the exponential radial basis kernel function and penalty factor obtained by using the IGSA algorithm has higher prediction accuracy. Compared with five conventional combined models such as EMD-WNN and EMD-PSO-LSSVM,the com-bined model EEMD-IGSA-LSSVM of exponential radial basis kernel function mentioned above can forecast wind power in an effective and accurate way.关键词
集合经验模态分解/风功率预测/最小二乘向量机/改进引力搜索算法/指数径向基核函数Key words
ensemble empirical mode decomposition (EEMD)/wind power prediction/least squares support vector machine (LSSVM)/improved gravitational search algorithm(IGSA)/exponential radial ba-sis function(ERBF)分类
建筑与水利引用本文复制引用
江岳春,杨旭琼,贺飞,陈礼锋,何钟南..基于EEMD-IGSA-LSSVM的超短期风电功率预测∗[J].湖南大学学报（自然科学版）,2016,43(10):70-78,9.基金项目
国家自然科学基金资助项目(51277057),National Natural Science Foundation of China(51277057) （51277057）
