基于ESMD-LSSVM模型的径流式水电站出力预测研究  

Research on Power Prediction of Runoff Hydropower Station Based on ESMD-LSSVM Model

作　　者：梁曦文 肖峰 闵昊凌 王世杰[1] LIANG Xi-wen;XIAO Feng;MIN Hao-ling;WANG Shi-jie(College of Water Resources and Hydropower Engineering,North China Electric Power University,Beijing 102206,China)

机构地区：[1]华北电力大学水利与水电工程学院,北京102206

出　　处：《中国农村水利水电》2023年第9期224-229,235,共7页China Rural Water and Hydropower

基　　金：国家自然科学基金项目(52030003)。

摘　　要：针对径流式水电站日出力随机性强,直接预测精度低的特点,采用极点对称模态分解(ESMD)对出力序列进行平稳化处理,结合最小二乘支持向量机(LSSVM),建立了基于ESMD-LSSVM的组合预测模型。选取西北某省径流式水电站2020年的日出力时间序列进行实例分析,并与单一模型SVM,LSSVM,BP及组合模型ESMD-SVM,ESMD-BP预测效果进行比较。结果发现:①PACF分析得到ESMD分解后的各子序列的特征向量不同,反映了径流式水电站日出力的复杂性和多变性的特征。②与单一模型相比,组合模型泛化能力更强,对时间序列中出力突变点的预测更准确。③ESMD-LSSVM组合模型日出力预测效果较好,为径流式水电站日出力时间序列预测提供了新的方法参考。In view of the characteristics of strong randomness and low direct prediction accuracy of daily power of runoff hydropower stations,this paper uses the Extreme-point Symmetric Mode Decomposition(ESMD)to smooth the power sequence.Combined with the Least-Square Support Vector Machines(LSSVM),a combined prediction model based on ESMD-LSSVM is established.The daily power time series of a runoff hydropower station in Northwest China in 2020 is selected for example analysis,and compared with the prediction results of single model SVM,LSSVM,BP and combined model ESMD-SVM,ESMD-BP.The results show that:①PACF analysis shows that the feature vec⁃tors of each subsequence after ESMD decomposition are different,which reflects the complexity and variability of the daily power of runoff hy⁃dropower stations.②Compared with the single model,the combined model has stronger generalization ability and more accurate prediction of the power mutation point in the time series.③The daily power of ESMD-LSSVM combined model has good prediction effect,which provides a new method reference for the daily power time series prediction of runoff hydropower station.

关 键 词：ESMD 发电功率预测 LSSVM 模型 

分 类 号：TV747[水利工程—水利水电工程]