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Prediction interval estimation of electricity prices using PSO-tuned support vector machines

Shrivastava, Nitin Anand, Khosravi, Abbas and Panigrahi, Bijaya Ketan 2015, Prediction interval estimation of electricity prices using PSO-tuned support vector machines, IEEE transactions on industrial informatics, vol. 11, no. 2, pp. 322-331, doi: 10.1109/TII.2015.2389625.

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Title Prediction interval estimation of electricity prices using PSO-tuned support vector machines
Author(s) Shrivastava, Nitin Anand
Khosravi, AbbasORCID iD for Khosravi, Abbas orcid.org/0000-0001-6927-0744
Panigrahi, Bijaya Ketan
Journal name IEEE transactions on industrial informatics
Volume number 11
Issue number 2
Start page 322
End page 331
Total pages 10
Publisher IEEE
Place of publication Piscataway, N.J.
Publication date 2015-04-27
ISSN 1551-3203
Keyword(s) Electricity market
Particle swarm optimization
Prediction interval
Support vector machines
Uncertainty
Science & Technology
Technology
Automation & Control Systems
Computer Science, Interdisciplinary Applications
Engineering, Industrial
Computer Science
Engineering
particle swarm optimization (PSO)
prediction interval (PI)
support vector machines (SVM)
NEURAL-NETWORKS
CONFIDENCE
OPTIMIZATION
MARKET
Summary Uncertainty of the electricity prices makes the task of accurate forecasting quite difficult for the electricity market participants. Prediction intervals (PIs) are statistical tools which quantify the uncertainty related to forecasts by estimating the ranges of the future electricity prices. Traditional approaches based on neural networks (NNs) generate PIs at the cost of high computational burden and doubtful assumptions about data distributions. In this work, we propose a novel technique that is not plagued with the above limitations and it generates high-quality PIs in a short time. The proposed method directly generates the lower and upper bounds of the future electricity prices using support vector machines (SVM). Optimal model parameters are obtained by the minimization of a modified PI-based objective function using a particle swarm optimization (PSO) technique. The efficiency of the proposed method is illustrated using data from Ontario, Pennsylvania-New Jersey-Maryland (PJM) interconnection day-ahead and real-time markets.
Language eng
DOI 10.1109/TII.2015.2389625
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
ERA Research output type C Journal article
Copyright notice ©2015, IEEE
Persistent URL http://hdl.handle.net/10536/DRO/DU:30075809

Document type: Journal Article
Collection: Centre for Intelligent Systems Research
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Created: Wed, 26 Aug 2015, 09:03:32 EST

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