Short-term load forecasting method based on RBF neural network and ANFIS system

被引：0
|
作者：
Lei, Shao-Lan [1 ]
Sun, Cai-Xin [1 ]
Zhou, Quan [1 ]
Zhang, Xiao-Xing [1 ]
Cheng, Qiyun [1 ]
机构：
[1] Laboratory of High Voltage Engineering and Electrical New Technology, Chongqing University, Chongqing 400030, China
来源：
Zhongguo Dianji Gongcheng Xuebao/Proceedings of the Chinese Society of Electrical Engineering | 2005年 / 25卷 / 22期
关键词：
Adaptive control systems - Electric power systems - Mathematical models - Radial basis function networks - Real time systems;
D O I：
暂无
中图分类号：
学科分类号：
摘要：
To counter the influence of real-time electric price on short-term load, a model for forecasting the short-term load is set up by combining Radial Basis Function (RBF) neural network with Adaptive Neural Fuzzy Inference System (ANFIS). The model first draws on the nonlinear approaching capacity of the RBF network to forecast the load on the prediction day which takes no account of the factor of electric price, and then, based on the recent changes of real-time price, uses the ANFIS system to modify the results of load forecasting obtained by using the RBF network so as to improve the forecasting accuracy and overcome the defect of the RBF network in price-sensitive environment. As the results of an example of factual forecasting show the model presented in this paper can work effectively.
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页码：78 / 82
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