On the Quality and Value of Probabilistic Forecasts of Wind Generation

While most of the current forecasting methods provide single estimates of future wind generation, some methods now allow one to have probabilistic predictions of wind power. They are often given in the form of prediction intervals or quantile forecasts. Such forecasts, since they include the uncertainty information, can be seen as optimal for the management or trading of wind generation. This paper explores the differences and relations between the quality (i.e. statistical performance) and the operational value of these forecasts. An application is presented on the use of probabilistic predictions for bidding in a European electricity market. The benefits of a probabilistic view of wind power forecasting are clearly demonstrated

[1]  Henrik Madsen,et al.  Using quantile regression to extend an existing wind power forecasting system with probabilistic forecasts , 2006 .

[2]  Peter Hall,et al.  Improving coverage accuracy of nonparametric prediction intervals , 2001 .

[3]  G. Strbac,et al.  Trading Wind Generation in Short-Term Energy Markets , 2002, IEEE Power Engineering Review.

[4]  Arthouros Zervos,et al.  Developing wind energy to meet the Kyoto targets in the European union , 2003 .

[5]  A. Raftery,et al.  Probabilistic forecasts, calibration and sharpness , 2007 .

[6]  Henrik Madsen,et al.  Wind power Ensemble forecasting , 2004 .

[7]  A. H. Murphy,et al.  What Is a Good Forecast? An Essay on the Nature of Goodness in Weather Forecasting , 1993 .

[8]  J. Usaola,et al.  Benefits for Wind Energy in Electricity Markets from Using Short Term Wind Power Prediction Tools; A Simulation Study , 2004 .

[9]  Peter F. Christoffersen Evaluating Interval Forecasts , 1998 .

[10]  Sven-Erik Thor,et al.  Long‐term research and development needs for wind energy for the time frame 2000–2020 , 2002 .

[11]  R. Baillie,et al.  Prediction in dynamic models with time-dependent conditional variances , 1992 .

[12]  P. Pinson,et al.  Uncertainty of short-term wind power forecasts a methodology for on-line assessment , 2004, 2004 International Conference on Probabilistic Methods Applied to Power Systems.

[13]  Alexander Boogert,et al.  On the effectiveness of the anti-gaming policy between the day-ahead and real-time electricity markets in The Netherlands , 2005 .

[14]  A. Raftery,et al.  Strictly Proper Scoring Rules, Prediction, and Estimation , 2007 .

[15]  Pierre Pinson,et al.  On‐line assessment of prediction risk for wind power production forecasts , 2003 .

[16]  Henrik Madsen,et al.  Properties of quantile and interval forecasts of wind generation and their evaluation. , 2006 .

[17]  Georges Kariniotakis,et al.  Optimizing Benefits from wind power participation in electricity market using advanced tools for wind power forecasting and uncertainty assessment , 2004 .