Prediction intervals for short-term wind farm power generation forecasts

Khosravi, Abbas, Nahavandi, Saeid and Creighton, Doug 2013, Prediction intervals for short-term wind farm power generation forecasts, IEEE Transactions on sustainable energy, vol. 4, no. 3, pp. 602-610, doi: 10.1109/TSTE.2012.2232944.

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Title Prediction intervals for short-term wind farm power generation forecasts
Author(s) Khosravi, AbbasORCID iD for Khosravi, Abbas
Nahavandi, SaeidORCID iD for Nahavandi, Saeid
Creighton, DougORCID iD for Creighton, Doug
Journal name IEEE Transactions on sustainable energy
Volume number 4
Issue number 3
Start page 602
End page 610
Total pages 9
Publisher IEEE
Place of publication Piscataway, N.J.
Publication date 2013
ISSN 1949-3029
Keyword(s) neural networks
prediction intervals
wind energy
Summary Quantification of uncertainties associated with wind power generation forecasts is essential for optimal management of wind farms and their successful integration into power systems. This paper investigates two neural network-based methods for direct and rapid construction of prediction intervals (PIs) for short-term forecasting of power generation in wind farms. The lower upper bound estimation and bootstrap methods are used to quantify uncertainties associated with forecasts. The effectiveness and efficiency of these two general methods for uncertainty quantification is examined using twenty four month data from a wind farm in Australia. PIs with a confidence level of 90% are constructed for four forecasting horizons: five, ten, fifteen, and thirty minutes. Quantitative measures are applied for objective evaluation and unbiased comparison of PI quality. Demonstrated results indicate that reliable PIs can be constructed in a short time without resorting to complicate computational methods or models. Also quantitative comparison reveals that bootstrap PIs are more suitable for short prediction horizon, and lower upper bound estimation PIs are more appropriate for longer forecasting horizons.
Language eng
DOI 10.1109/TSTE.2012.2232944
Field of Research 080108 Neural, Evolutionary and Fuzzy Computation
Socio Economic Objective 850509 Wind Energy
HERDC Research category C1 Refereed article in a scholarly journal
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