Evaluation of cosine radial basis function neural networks on electric power load forecasting

被引：0

作者：

Karayiannis, NB ^{[1
]}

Balasubramanian, M ^{[1
]}

Malki, HA ^{[1
]}

机构：

[1] Univ Houston, Dept Elect & Comp Engn, Houston, TX 77204 USA

来源：

PROCEEDINGS OF THE INTERNATIONAL JOINT CONFERENCE ON NEURAL NETWORKS 2003, VOLS 1-4 | 2003年

关键词：

D O I：

暂无

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

This paper presents the results of a study aimed at the development of a system for short-term electric power load forecasting. This was attempted by training feedforward neural networks (FFNNs) and cosine radial basis function (RBF) neural networks to predict future power demand based on past power load data and weather conditions. This comparison indicates that both neural network models exhibit comparable performance when tested on the training data but cosine RBF neural networks generalize better since they outperform considerably FFNNs on the testing data.

引用

页码：2100 / 2105

页数：6

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