SHORT-TERM LOAD FORECASTING USING FUZZY NEURAL NETWORKS

被引：123
|
作者：
BAKIRTZIS, AG
THEOCHARIS, JB
KIARTZIS, SJ
SATSIOS, KJ
机构：
[1] Department of Electrical and Computer Engineering Aristotle, University of Thessaloniki
来源：
IEEE TRANSACTIONS ON POWER SYSTEMS | 1995年 / 10卷 / 03期
关键词：
LOAD FORECASTING; FUZZY SYSTEMS; FUZZY NEURAL NETWORKS;
D O I：
10.1109/59.466494
中图分类号：
TM [电工技术]; TN [电子技术、通信技术];
学科分类号：
0808 ; 0809 ;
摘要：
This paper presents the development of a fuzzy system for short term load forecasting. The fuzzy system has the network structure and the training procedure of a neural network and is called Fuzzy Neural Network (FNN). A FNN initially creates a rule base from existing historical load data. The parameters of the rule base are then tuned through a training process, so that the output of the FNN adequately matches the available historical load data. Once trained, the FNN can be used to forecast future loads. Test results show that the FNN can forecast future loads with an accuracy comparable to that of neural networks, while its training is much faster than that of neural networks.
引用
收藏
页码：1518 / 1524
页数：7
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