Prediction interval estimation for electricity price and demand using support vector machines

Shrivastava,NA, Khosravi,A and Panigrahi,BK 2014, Prediction interval estimation for electricity price and demand using support vector machines, in Proceedings of the International Joint Conference on Neural Networks, IEEE, Piscataway, N.J., pp. 3995-4002, doi: 10.1109/IJCNN.2014.6889745.

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Title Prediction interval estimation for electricity price and demand using support vector machines
Author(s) Shrivastava,NA
Khosravi,AORCID iD for Khosravi,A orcid.org/0000-0001-6927-0744
Panigrahi,BK
Conference name International Joint Conference on Neural Networks (2014 : Beijing, China)
Conference location Beijing, China
Conference dates 6-11 Jul. 2014
Title of proceedings Proceedings of the International Joint Conference on Neural Networks
Editor(s) [Unknown]
Publication date 2014
Conference series International Joint Conference on Neural Networks
Start page 3995
End page 4002
Total pages 8
Publisher IEEE
Place of publication Piscataway, N.J.
Keyword(s) Deregulation
Particle swarm optimization
Prediction interval
Support vector machines
Uncertainty
Summary Uncertainty is known to be a concomitant factor of almost all the real world commodities such as oil prices, stock prices, sales and demand of products. As a consequence, forecasting problems are becoming more and more challenging and ridden with uncertainty. Such uncertainties are generally quantified by statistical tools such as prediction intervals (Pis). Pis quantify the uncertainty related to forecasts by estimating the ranges of the targeted quantities. Pis generated by traditional neural network based approaches are limited by high computational burden and impractical assumptions about the distribution of the data. A novel technique for constructing high quality Pis using support vector machines (SVMs) is being proposed in this paper. The proposed technique directly estimates the upper and lower bounds of the PI in a short time and without any assumptions about the data distribution. The SVM parameters are tuned using particle swarm optimization technique by minimization of a modified Pi-based objective function. Electricity price and demand data of the Ontario electricity market is used to validate the performance of the proposed technique. Several case studies for different months indicate the superior performance of the proposed method in terms of high quality PI generation and shorter computational times.
ISBN 9781479914845
Language eng
DOI 10.1109/IJCNN.2014.6889745
Field of Research 080108 Neural, Evolutionary and Fuzzy Computation
Socio Economic Objective 850699 Energy Storage
HERDC Research category E1 Full written paper - refereed
ERA Research output type E Conference publication
Copyright notice ©2014, IEEE
Persistent URL http://hdl.handle.net/10536/DRO/DU:30072559

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