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Prediction interval estimation for wind farm power generation forecasts using support vector machines
conference contributionposted on 2015-01-01, 00:00 authored by N A Shrivastava, Abbas KhosraviAbbas Khosravi, B K Panigrahi
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.
EventInternational Joint Conference on Neural Networks (2015 : Killarney, Ireland)
Pagination1 - 7
Place of publicationPiscataway, N.J.
Publication classificationE Conference publication; E1 Full written paper - refereed
Copyright notice2015, IEEE
Title of proceedingsIJCNN 2015 : Proceedings of the International Joint Conference on Neural Networks
CategoriesNo categories selected