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

Content Provider	IEEE Xplore Digital Library
Author	Shrivastava, N.A. Khosravi, A. Panigrahi, B.K.
Copyright Year	2014
Description	Author affiliation: Dept. of Electr. Eng., Indian Inst. of Technol., New Delhi, New Delhi, India (Shrivastava, N.A.; Panigrahi, B.K.) \|\| Center for Intell. Syst. Res., Deakin Univ., Geelong, VIC, Australia (Khosravi, A.)
Abstract	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.
Starting Page	3995
Ending Page	4002
File Size	675481
Page Count	8
File Format	PDF
ISSN	21614407
e-ISBN	9781479914845
DOI	10.1109/IJCNN.2014.6889745
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2014-07-06
Publisher Place	China
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Support vector machines Artificial neural networks Electricity Training Optimization Uncertainty Training data Deregulation Particle swarm optimization Prediction interval
Content Type	Text
Resource Type	Article

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