Quantile Forecasting of Wind Power Using Variability Indices

Georgios Anastasiades and Patrick McSharry ()
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Georgios Anastasiades: Mathematical Institute, University of Oxford, 24-29 St Giles', OX1 3LB, Oxford, UK

Energies, 2013, vol. 6, issue 2, 1-34

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 (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2013
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (8)

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