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):. Jia Yilun,Gong Qingwu,Li Junxiong,Zhan Jinsong.The Power Load Combined Forecasting Based on CEEMDAN and QPSO-SVM[J].Electrical Measurement & Instrumentation,2017,54(1):.
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
To predict the power system load more accurately, this article proposes a combined forecast method based on the complete ensemble empirical mode decomposition with adaptive noise and quantum particle swarm optimization. Firstly, aiming at the modes overlap problem and signal distortion existing in ensemble empirical mode decomposition, this paper proposes the complete ensemble empirical mode decomposition with adaptive noise, decomposes the original signal into the different time scale. Then it uses the support vector machine to predict the decomposition result, and employs the quantum particle swarm optimization method to optimize the insensitive loss coefficient, 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, which proves the validity and practicability of the method mentioned in this paper.
