Neural networks for short-term load forecasting: a review and evaluation

Author

Hippert, Henrique Steinherz ; Pedreira, Carlos Eduardo ; Souza, Reinaldo Castro

Author_Institution

Dept. of Stat., Univ. Federal de Juiz de Fora, Brazil

Volume

16

Issue

1

fYear

2001

fDate

2/1/2001 12:00:00 AM

Firstpage

44

Lastpage

55

Abstract

Load forecasting has become one of the major areas of research in electrical engineering, and most traditional forecasting models and artificial intelligence techniques have been tried out in this task. Artificial neural networks (NNs) have lately received much attention, and a great number of papers have reported successful experiments and practical tests with them. Nevertheless, some authors remain skeptical, and believe that the advantages of using NNs in forecasting have not been systematically proved yet. In order to investigate the reasons for such skepticism, this review examines a collection of papers (published between 1991 and 1999) that report the application of NNs to short-term load forecasting. Our aim is to help to clarify the issue, by critically evaluating the ways in which the NNs proposed in these papers were designed and tested

Keywords

load forecasting; neural nets; power system analysis computing; artificial intelligence techniques; artificial neural networks; multilayer perceptrons; neural networks; overfitting; short-term load forecasting; Artificial neural networks; Costs; Economic forecasting; Electrical engineering; Electricity supply industry; Load forecasting; Multi-layer neural network; Neural networks; Predictive models; Testing;

fLanguage

English

Journal_Title

Power Systems, IEEE Transactions on

Publisher

ieee

ISSN

0885-8950

Type

jour

DOI

10.1109/59.910780

Filename

910780