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Uncertainty quantification for wind farm power generation
conference contribution
posted on 2012-01-01, 00:00 authored by Abbas KhosraviAbbas Khosravi, Saeid Nahavandi, Douglas CreightonDouglas Creighton, R NaghavizadehAccurate forecasting of wind farm power generation is essential for successful operation and management of wind farms and to minimize risks associated with their integration into energy systems. However, due to the inherent wind intermittency, wind power forecasts are highly prone to error and often far from being perfect. The purpose of this paper is to develop statistical methods for quantifying uncertainties associated with wind power generation forecasts. Prediction intervals (PIs) with a prescribed confidence level are constructed using the delta and bootstrap methods for neural network forecasts. The moving block bootstrap method is applied to preserve the correlation structure in wind power observations. The effectiveness and efficiency of these two methods for uncertainty quantification is examined using two month datasets taken from a wind farm in Australia. It is demonstrated that while all constructed PIs are theoretically valid, bootstrap PIs are more informative than delta PIs, and are therefore more useful for decision-making.
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Event
International Joint Conference on Neural Networks (2012 : Brisbane, Qld.)Pagination
309 - 314Publisher
IEEELocation
Brisbane, Qld.Place of publication
[Piscataway, N. J.]Start date
2012-06-10End date
2012-06-15ISBN-13
9781467314893Language
engPublication classification
E1 Full written paper - refereedCopyright notice
2012, IEEETitle of proceedings
IJCNN/WCCI 2012 : Proceedings of the 2012 International Joint Conference on Neural NetworksUsage metrics
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