A new artificial intelligent peak power load forecaster based on non-fixed neural networks

被引：53

作者：

Huang, HC ^{[1
]}

Hwang, RC ^{[1
]}

Hsieh, JG ^{[1
]}

机构：

[1] Natl Sun Yat Sen Univ, Dept Elect Engn, Kaohsiung 80424, Taiwan

来源：

INTERNATIONAL JOURNAL OF ELECTRICAL POWER & ENERGY SYSTEMS | 2002年 / 24卷 / 03期

关键词：

non-fixed neural networks; gray analysis; stochastic back-propagation learning;

D O I：

10.1016/S0142-0615(01)00026-6

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：

In this paper, a new artificial intelligent peak power load forecaster constructed by non-fixed neural networks (NNs) is developed. Several techniques, including gray analysis and stochastic back-propagation learning rule with dynamic learning rate and momentum, are used in this forecaster in order to attain more accurate prediction in forecasting operation. As a comparison, several models including recursive time series model, ANNSTLF module and fixed size NNs with constant learning rate and momentum are also performed for demonstrating the advantages of our proposed forecaster. (C) 2002 Elsevier Science Ltd. All rights reserved.

引用

页码：245 / 250

页数：6

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