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Quantile Forecasting of Wind Power Using Variability Indices
Mathematical Institute, University of Oxford, 24-29 St Giles', OX1 3LB, Oxford, UK
Smith School of Enterprise and the Environment, University of Oxford, Hayes House, 75 George Street, OX1 2BQ, Oxford, UK
* Author to whom correspondence should be addressed.
Received: 23 November 2012; in revised form: 12 January 2013 / Accepted: 22 January 2013 / Published: 5 February 2013
Abstract: Wind power forecasting techniques have received substantial attention recently due to the increasing penetration of wind energy in national power systems. While the initial focus has been on point forecasts, the need to quantify forecast uncertainty and communicate the risk of extreme ramp events has led to an interest in producing probabilistic forecasts. Using four years of wind power data from three wind farms in Denmark, we develop quantile regression models to generate short-term probabilistic forecasts from 15 min up to six hours ahead. More specifically, we investigate the potential of using various variability indices as explanatory variables in order to include the influence of changing weather regimes. These indices are extracted from the same wind power series and optimized specifically for each quantile. The forecasting performance of this approach is compared with that of appropriate benchmark models. Our results demonstrate that variability indices can increase the overall skill of the forecasts and that the level of improvement depends on the specific quantile.
Keywords: wind power forecasting; wind power variability; quantile forecasting; density forecasting; quantile regression; continuous ranked probability score; quantile loss function; check function
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MDPI and ACS Style
Anastasiades, G.; McSharry, P. Quantile Forecasting of Wind Power Using Variability Indices. Energies 2013, 6, 662-695.
Anastasiades G, McSharry P. Quantile Forecasting of Wind Power Using Variability Indices. Energies. 2013; 6(2):662-695.
Anastasiades, Georgios; McSharry, Patrick. 2013. "Quantile Forecasting of Wind Power Using Variability Indices." Energies 6, no. 2: 662-695.