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Construction of optimal prediction intervals for load forecasting problems

Khosravi, Abbas, Nahavandi, Saeid and Creighton, Doug 2010, Construction of optimal prediction intervals for load forecasting problems, IEEE transactions on power systems, vol. 25, no. 3, pp. 1496-1503.

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Title Construction of optimal prediction intervals for load forecasting problems
Author(s) Khosravi, Abbas
Nahavandi, Saeid
Creighton, Doug
Journal name IEEE transactions on power systems
Volume number 25
Issue number 3
Start page 1496
End page 1503
Total pages 8
Publisher IEEE
Place of publication Piscataway, N.J.
Publication date 2010-03-11
ISSN 0885-8950
1558-0679
Keyword(s) load forecasting
neural network
prediction interval
Summary Short-term load forecasting is fundamental for the reliable and efficient operation of power systems. Despite its importance, accurate prediction of loads is problematic and far remote. Often uncertainties significantly degrade performance of load forecasting models. Besides, there is no index available indicating reliability of predicted values. The objective of this study is to construct prediction intervals for future loads instead of forecasting their exact values. The delta technique is applied for constructing prediction intervals for outcomes of neural network models. Some statistical measures are developed for quantitative and comprehensive evaluation of prediction intervals. According to these measures, a new cost function is designed for shortening length of prediction intervals without compromising their coverage probability. Simulated annealing is used for minimization of this cost function and adjustment of neural network parameters. Demonstrated results clearly show that the proposed methods for constructing prediction interval outperforms the traditional delta technique. Besides, it yields prediction intervals that are practically more reliable and useful than exact point predictions.
Notes Date of Publication: 11 March 2010. This material is presented to ensure timely dissemination of scholarly and technical work. Copyright and all rights therein are retained by authors or by other copyright holders. All persons copying this information are expected to adhere to the terms and constraints invoked by each author's copyright. In most cases, these works may not be reposted without the explicit permission of the copyright holder.
Language eng
Field of Research 080108 Neural, Evolutionary and Fuzzy Computation
Socio Economic Objective 970109 Expanding Knowledge in Engineering
HERDC Research category C1 Refereed article in a scholarly journal
HERDC collection year 2010
Copyright notice ©2010, IEEE
Persistent URL http://hdl.handle.net/10536/DRO/DU:30030994

Document type: Journal Article
Collections: Centre for Intelligent Systems Research
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Created: Mon, 01 Nov 2010, 16:27:47 EST by Sandra Dunoon