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. Attached Files Name Description MIMEType Size Downloads Title Prediction interval estimation for electricity price and demand using support vector machines Author(s) Shrivastava,NA 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 Related Links Link Description Connect to published version Go to link with your DU access privileges     Unless expressly stated otherwise, the copyright for items in DRO is owned by the author, with all rights reserved. Versions Version Filter Type Tue, 21 Apr 2015, 17:17:37 EST Tue, 21 Apr 2015, 17:18:25 EST Thu, 14 May 2015, 11:42:00 EST Fri, 15 May 2015, 05:00:07 EST Fri, 15 May 2015, 06:27:08 EST Mon, 18 May 2015, 16:37:40 EST Mon, 18 May 2015, 16:41:01 EST Wed, 27 May 2015, 14:02:58 EST Mon, 18 Jul 2016, 15:36:01 EST Thu, 25 Jan 2018, 11:09:30 EST Filtered Full Citation counts:   Cited 3 times in TR Web of Science Cited 5 times in Scopus Search Google Scholar Access Statistics: 179 Abstract Views, 3 File Downloads  -  Detailed Statistics Created: Thu, 14 May 2015, 11:42:00 EST

Every reasonable effort has been made to ensure that permission has been obtained for items included in DRO. If you believe that your rights have been infringed by this repository, please contact drosupport@deakin.edu.au.