Short term load forecasting by using wavelet neural networks

被引：0

作者：

Bashir, Z ^{[1
]}

El-Hawary, ME ^{[1
]}

机构：

[1] Dalhousie Univ, DalTeachTUNS, Dept Elect & Comp Engn, Halifax, NS B3J 2X4, Canada

来源：

2000 CANADIAN CONFERENCE ON ELECTRICAL AND COMPUTER ENGINEERING, CONFERENCE PROCEEDINGS, VOLS 1 AND 2: NAVIGATING TO A NEW ERA | 2000年

关键词：

D O I：

暂无

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：

The application of the wavelet neural networks (WNNs) to short-term load forecasting is reported in this work. The wavelet neural network has much higher ability of generalization and fasts convergence for learning than a mulitlayer feedforward neural network. The Morlet wavelet has been chosen in this study as the activation function. The 3-layer backpropagation algorithm is used to train the network by learning the nonlinear relationship between input and output of the network. The input data consists of historical load and weather information, which are collected over a period of 2-years (1994-1995) to train the network and data of one year (1996) is used to test the network. The results of the network have been compared with artificial neural network and show an improved forecast with fast convergence.

引用

页码：163 / 166

页数：4

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