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

Version 2 2024-06-04, 02:17
Version 1 2016-04-18, 12:38
conference contribution
posted on 2024-06-04, 02:17 authored by NA Shrivastava, Abbas KhosraviAbbas Khosravi, BK 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.

History

Pagination

1-7

Location

Killarney, Ireland

Start date

2015-07-12

End date

2015-07-17

ISBN-13

9781479919604

Language

eng

Publication classification

E Conference publication, E1 Full written paper - refereed

Copyright notice

2015, IEEE

Title of proceedings

IJCNN 2015 : Proceedings of the International Joint Conference on Neural Networks

Event

International Joint Conference on Neural Networks (2015 : Killarney, Ireland)

Publisher

IEEE

Place of publication

Piscataway, N.J.