电力系统短期负荷预报的小波-神经网络-PARIMA方法  

WAVELET-NEURAL NETWORKS-PARIMA METHOD FOR POWER SYSTEM SHORT TERM LOAD FORECASTING

作　　者：冉启文[1] 单永正[1] 王骐[1] 王建赜[1] 

机构地区：[1]哈尔滨工业大学,黑龙江哈尔滨150001

出　　处：《中国电机工程学报》2003年第3期38-42,68,共6页Proceedings of the CSEE

摘　　要：针对电力系统负荷具有拟周期性、非平稳性、非线性等特点,提出一种小波-神经网络-PARIMA模型并研究它在电力系统短期负荷预报中的应用:利用小波变换提取和分离负荷的各种隐周期和非线性,把小波分解的特性和分解数据随尺度倍增而倍减的规律用于感知机神经网络(MLP)和周期自回归移动模型(PARIMA)的建模,各尺度小波分解用MLP进行建模和预报,最大尺度上的尺度分解用 PARIMA进行建模和预报。最后,利用径向基函数网络(RBF)将各尺度域的预报结果组合成为负荷最终预报。实例说明,该方法能够揭示负荷的拟周期性、非平稳性、非线性,在电力系统短期负荷预报中的应用是成功的和有效的。A wavelet-neural networks-PARIMA (Periodi-cal Auto-Regressive Integral Moving Average) method is proposed based on the quasi-periodicity, nonstationarity, nonlinearity of the load of power systems and so on and applied to the forecast of the power system short term load. Every kind of hidden periodicity and nonlinearity of the load can be extracted and separated by using the wavelettransform. There is a rule that decomposition data decrease doubly while scales increase doubly. According to this rule and the characteristics of wavelet decompositions can be build MLPneural network model. Each scale wavelet transform ismodeled and forecasted by using the PARIMA and a MLP. The scale transform of the most scale of the original signal ismodeled and forecasted by using the PARIMA model. Finally, these forecasts in scale domains is synthesized to the system load forecasts by using a RBF neural network. The results of a practical example shows that the proposed method is able to reveal the quasi-periodicity, nonstationarity and nonlinearity of the power system load.

关 键 词：电力系统 短期负荷预报 小波 神经网络 PARIMA方法 尺度函数 

分 类 号：TM715[电气工程—电力系统及自动化]