电力系统短期负荷预测组合NN模型的研究与应用  

Research and Application of Combined NN Model for Short-Term Load Forecasting in Electrical Power System

作　　者：张伟[1] 徐超 韩华 张智晟[1] 

机构地区：[1]青岛大学自动化工程学院,山东青岛266071 [2]青岛供电公司,山东青岛266002

出　　处：《青岛大学学报（工程技术版）》2011年第2期8-13,共6页Journal of Qingdao University(Engineering & Technology Edition)

基　　金：山东电力集团公司科技计划资助项目(2010A-41);山东省教育厅科技计划资助项目(J07WJ10)

摘　　要：针对单一预测模型不能完全反映电力负荷的变化规律和信息的问题,构造了组合NN预测模型,该模型由基于附加动量算法的AM-NN子模型和基于拟牛顿算法的QN-NN子模型构成,通过时变综合权系数将两个模型融合在一起,将气象因素数据引入到模型中,采用滚动优化策略,使模型具有较好的泛化性和收敛性。通过对实际电网负荷的预测仿真和测试,证实所提出的组合NN预测模型能有效提高预测精度,平均绝对误差和最大相对误差分别达到了1.84%和5.77%,将其作为实际电网短期负荷预测软件的预测子模块,能满足实际的调度预测要求。For only one model cannot totally reflect the changing rules and information of power load,a combined NN forecasting model is constructed in this paper.This model is composed of AM-NN sub-model which is based on the additional momentum algorithm and QN-NN sub-model which ground on Quasi-Newton algorithm.By means of time-variant comprehensive weight,the model compromises two sub-models.Simultaneously,weather factors are led in the model.Adopting the strategy of rolling optimization makes this model be better at generalization and convergence.The combination model is used in the load forecasting simulating and testing in power grid,the results prove that it can effectively improve forecasting accuracy,and mean absolute percentage error and maximum relative error are respectively 1.84%and 5.77%.It is used as a forecasting sub-modular of short-term load forecasting soft-ware in power grid,which can meet the actual demands of scheduling predictability.

关 键 词：电力系统 短期负荷预测 组合神经网络 附加动量算法 拟牛顿算法 时变综合权系数 

分 类 号：TM715.1[电气工程—电力系统及自动化]