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

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 paper...

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Bibliographic Details
Published in:IEEE transactions on power systems 2001-02, Vol.16 (1), p.44-55
Main Authors: Hippert, H.S., Pedreira, C.E., Souza, R.C.
Format: Article
Language:English
Subjects:
Artificial intelligence
Artificial neural networks
Collection
Costs
Economic forecasting
Electric power generation
Electrical engineering
Electricity supply industry
Expert systems
Forecasting
Load forecasting
Multi-layer neural network
Neural networks
Predictive models
Tasks
Testing
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Summary: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.
ISSN:0885-8950
1558-0679
DOI:10.1109/59.910780