基于EEMD-LSSVM的超短期负荷预测  

Ultra-short-term load forecasting based on EEMD-LSSVM

作　　者：王新[1] 孟玲玲[1] 

机构地区：[1]河南理工大学电气工程与自动化学院,河南焦作454000

出　　处：《电力系统保护与控制》2015年第1期61-66,共6页Power System Protection and Control

基　　金：河南省科技攻关项目(142102210048);河南省高校科技创新人才支持计划项目(2008HASTIT022)

摘　　要：针对传统的最小二乘支持向量机(LSSVM)参数不易确定且单一预测模型精度不高的问题,提出了一种基于集合经验模态分解(EEMD)与LSSVM的组合预测模型。首先利用EEMD将历史数据分解成一系列相对比较平稳的分量序列,再对各子序列分别建立合适的预测模型。进一步通过贝叶斯证据框架来优化LSSVM的参数,用贝叶斯推理确定模型参数、正规化超参数和核参数。然后将各子序列预测结果进行叠加得到最终预测值。最后,将该预测模型用于某一家庭超短期负荷预测中,仿真结果表明,该模型取得了比单一模型更好的预测效果。To solve the problem of the uncertain parameters and the low precision of the single forecasting model for the traditional least squares support vector machine （LSSVM）, a combined forecasting model based on ensemble empirical mode decomposition （EEMD） and the LSSVM is proposed. Firstly, the historical data is decomposed into a series of relatively stable component of the sequence by the EEMD, and then the appropriate forecasting model is established for each component of the sequence. The parameters of the LSSVM are optimized through the Bayesian evidence framework. Bayesian inferences are used to determine model parameters, regularization hyper-parameters and kernel parameters. The results of each component forecasting are superimposed to obtain the final forecasting result. Finally, a household ultra-short-term load data is used to validate the model, and the simulation results show that this model has achieved better forecasting result than a single model.

关 键 词：超短期负荷预测 集合经验模态分解 最小二乘支持向量机 贝叶斯框架 时间序列 

分 类 号：TM715[电气工程—电力系统及自动化]