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Prediction interval estimation for wind farm power generation forecasts using support vector machines

Shrivastava, Nitin Anand, Khosravi, Abbas and Panigrahi, B.K. 2015, Prediction interval estimation for wind farm power generation forecasts using support vector machines, in IJCNN 2015 : Proceedings of the International Joint Conference on Neural Networks, IEEE, Piscataway, N.J., pp. 1-7, doi: 10.1109/IJCNN.2015.7280670.

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Title Prediction interval estimation for wind farm power generation forecasts using support vector machines
Author(s) Shrivastava, Nitin Anand
Khosravi, AbbasORCID iD for Khosravi, Abbas orcid.org/0000-0001-6927-0744
Panigrahi, B.K.
Conference name International Joint Conference on Neural Networks (2015 : Killarney, Ireland)
Conference location Killarney, Ireland
Conference dates 12-17 Jul. 2015
Title of proceedings IJCNN 2015 : Proceedings of the International Joint Conference on Neural Networks
Publication date 2015
Start page 1
End page 7
Total pages 7
Publisher IEEE
Place of publication Piscataway, N.J.
Keyword(s) predicition interval
support vector machine
uncertainty
wind power
Summary Accurate forecasting of wind power generation is quite an important as well as challenging task for the system operators and market participants due to its high uncertainty. It is essential to quantify uncertainties associated with wind power generation forecasts for their efficient application in optimal management of wind farms and integration into power systems. Prediction intervals (PIs) are well known statistical tools which are used to quantify the uncertainty related to forecasts by estimating the ranges of the future target variables. This paper investigates the application of a novel support vector machine based methodology to directly estimate the lower and upper bounds of the PIs without expensive computational burden and inaccurate assumptions about the distribution of the data. The efficiency of the method for uncertainty quantification is examined using monthly data from a wind farm in Australia. PIs for short term application are generated with a confidence level of 90%. Experimental results confirm the ability of the method in constructing reliable PIs without resorting to complex computational methods.
ISBN 9781479919604
Language eng
DOI 10.1109/IJCNN.2015.7280670
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
ERA Research output type E Conference publication
Copyright notice ©2015, IEEE
Persistent URL http://hdl.handle.net/10536/DRO/DU:30082897

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