Short term load forecasting using genetic algorithm and neural networks

被引：0

作者：

Heng, ETH ^{[1
]}

Srinivasan, D ^{[1
]}

Liew, AC ^{[1
]}

机构：

[1] Natl Univ Singapore, Dept Elect Engn, Singapore 119260, Singapore

来源：

PROCEEDINGS OF EMPD '98 - 1998 INTERNATIONAL CONFERENCE ON ENERGY MANAGEMENT AND POWER DELIVERY, VOLS 1 AND 2 AND SUPPLEMENT | 1998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：

This paper presents an Artificial Neural Network (ANN) model trained by a genetic algorithm (GA) for short term load forecasting. Genetic algorithms (GAs) seek to solve optimization problems using the methods of evolution, specifically survival of the fittest [1]. The software, Genehunter from Ward Systems Group was used to build a ANN model capable of forecasting one-day ahead hourly loads for weekdays and weekends. The proposed model is a three-layered feedforward backpropagation network. The results indicate that this model can achieve greater forecasting accuracy than the traditional statistical model. This ANN model has been implemented on real load data for the year 1995.

引用

页码：576 / 581

页数：6

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