电测与仪表2017，Vol.54Issue(1)：16-21,6.
基于CEEMDAN与量子粒子支持向量机的电力负荷组合预测
The power load combined forecasting based on CEEMDAN and QPSO-SVM
摘要
Abstract
To predict the power system load more accurately , this paper proposes a combined forecasting method based on the complete ensemble empirical mode decomposition with adaptive noise and quantum particle swarm opti -mization.Firstly, aiming at the modes overlap problem and signal distortion existing in ensemble empirical mode de -composition , this paper proposes the complete ensemble empirical mode decomposition with adaptive noise , and de-composes the original signals into the different time scales .Then, it uses the support vector machine to predict the de-composition result , and employs the quantum particle swarm optimization method to optimize the insensitive loss coeffi -cient, penalty coefficient and kernel function .Finally, by forecasting the power system load in a certain domain of Qinghai province and comparing it with another different methods , it proves the validity and practicability of the meth-od mentioned in this paper .关键词
经验模态分解/CEEMDAN/支持向量机/量子粒子群Key words
empirical mode decomposition/CEEMDAN/SVM/quantum particle swarm optimization分类
信息技术与安全科学引用本文复制引用
贾逸伦,龚庆武,李俊雄,占劲松..基于CEEMDAN与量子粒子支持向量机的电力负荷组合预测[J].电测与仪表,2017,54(1):16-21,6.基金项目
国家科技支撑项目(2013BAA02B01) （2013BAA02B01）
