Prediction intervals for electricity load forecasting using neural networks

Rana, Mashud, Koprinska, Irena, Khosravi, Abbas and Agelidis, Vassilios G. 2013, Prediction intervals for electricity load forecasting using neural networks, in IJCNN 2013 : Proceedings of the 2013 International Joint Conference on Neural Networks, IEEE, Piscataway, N.J., pp. 948-955.

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Title Prediction intervals for electricity load forecasting using neural networks
Author(s) Rana, Mashud
Koprinska, Irena
Khosravi, Abbas
Agelidis, Vassilios G.
Conference name International Joint Conference on Neural Networks (2013 : Dallas, Texas)
Conference location Dallas, Texas
Conference dates 4-9 Aug. 2013
Title of proceedings IJCNN 2013 : Proceedings of the 2013 International Joint Conference on Neural Networks
Editor(s) [Unknown]
Publication date 2013
Conference series International Joint Conference on Neural Networks
Start page 948
End page 955
Total pages 8
Publisher IEEE
Place of publication Piscataway, N.J.
Summary Most of the research in time series is concerned with point forecasting. In this paper we focus on interval forecasting and its application for electricity load prediction. We extend the LUBE method, a neural network-based method for computing prediction intervals. The extended method, called LUBEX, includes an advanced feature selector and an ensemble of neural networks. Its performance is evaluated using Australian electricity load data for one year. The results showed that LUBEX is able to generate high quality prediction intervals, using a very small number of previous lag variables and having acceptable training time requirements. The use of ensemble is shown to be critical for the accuracy of the results.
ISBN 9781467361286
9781467361293
Language eng
Field of Research 080108 Neural, Evolutionary and Fuzzy Computation
Socio Economic Objective 970108 Expanding Knowledge in the Information and Computing Sciences
HERDC Research category E1 Full written paper - refereed
Copyright notice ©2013, IEEE
Persistent URL http://hdl.handle.net/10536/DRO/DU:30057173

Document type: Conference Paper
Collection: Centre for Intelligent Systems Research
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