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• W1991420715 abstract "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." @default.
• W1991420715 created "2016-06-24" @default.
• W1991420715 creator A5015293969 @default.
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• W1991420715 date "2013-07-01" @default.
• W1991420715 modified "2023-10-14" @default.
• W1991420715 title "Prediction Intervals for Short-Term Wind Farm Power Generation Forecasts" @default.
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• W1991420715 doi "https://doi.org/10.1109/tste.2012.2232944" @default.
• W1991420715 hasPublicationYear "2013" @default.
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