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Prediction intervals for short-term wind farm power generation forecasts
journal contribution
posted on 2013-01-01, 00:00 authored by Abbas KhosraviAbbas Khosravi, Saeid Nahavandi, Douglas CreightonDouglas CreightonQuantification 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.
History
Journal
IEEE Transactions on sustainable energyVolume
4Issue
3Pagination
602 - 610Publisher
IEEELocation
Piscataway, N.J.Publisher DOI
ISSN
1949-3029eISSN
1949-3037Language
engPublication classification
C1 Refereed article in a scholarly journalUsage metrics
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No categories selectedKeywords
neural networksprediction intervalsuncertaintywind energyScience & TechnologyTechnologyGreen & Sustainable Science & TechnologyEnergy & FuelsEngineering, Electrical & ElectronicScience & Technology - Other TopicsEngineeringSUPPORT VECTOR MACHINESSPEED PREDICTIONENSEMBLE PREDICTIONSNEURAL-NETWORKSMODELS
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