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.
出处
《河海大学学报（自然科学版）》
CAS
CSCD
北大核心
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)
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
short-term electric load forecasting
load forecasting
time series
RBF-ARX model
cycle forecasting
structured nonlinear parameter optimization method
