基于RBF-ARX模型的短期电力负荷预测  

Short-term electric load forecasting based on RBF-ARX model

作　　者：侯海良[1,2] 孙妙平[1] 蔡斌军[1] 

机构地区：[1]中南大学信息科学与工程学院,湖南长沙410075 [2]湖南人文科技学院信息科学与工程系,湖南娄底417000

出　　处：《河海大学学报（自然科学版）》2015年第3期271-277,共7页Journal of Hohai University(Natural Sciences)

基　　金：国家自然科学基金(61075065;U1134108);湖南省教育厅优秀青年项目(13B014);湖南人文科技学院青年基金(2010QN11)

摘　　要：为了提高短期电力负荷预测的精度,提出基于RBF-ARX模型的短期电力负荷循环预测法:将短期电力负荷预测看作非线性时间序列预测问题,并根据历史负荷数据建立电力负荷自回归预测模型(ARX模型),用RBF神经网络逼近ARX模型的参数,并用结构化非线性参数优化法(SNPOM)离线估计模型参数。用该方法对湖南某市电力负荷进行预测,将预测结果与实际负荷值进行比较,结果表明:基于RBF-ARX模型的短期电力负荷循环预测法精度高,可靠性强,具有很好的实用性。In order to improve the accuracy of short-term electric load forecasting, a cycle forecasting method for short-term electric load forecasting is proposed based on a radial basis function network-style coefficients autoregressive model with an exogenous variable （ RBF-ARX） model. First, the short-term electric load forecasting was regarded as a nonlinear time series prediction problem, and an autoregressive model （ ARX model） of electric load forecasting was established based on historical load data. Then, the ARX model parameters were approximated with the RBF neural network and were estimated with an off-line structured nonlinear parameter optimization method （ SNPOM） . Finally, based on this, a cycle forecasting method for short-term electric load forecasting was established. The proposed method was used to predict the short-time electric load in a certain city of Hunan Province. The predicted results were compared with the actual load values. The results show that the proposed method has high accuracy, reliability, and practicability.

关 键 词：短期电力负荷 负荷预测 时间序列 RBF-ARX模型 循环预测 结构化非线性参数优化法 

分 类 号：TM714[电气工程—电力系统及自动化]