‘Good’ or ‘bad’ wind power forecasts: a relative concept

This paper reports a study on the importance of the training criteria for wind power forecasting and calls into question the generally assumed neutrality of the ‘goodness’ of particular forecasts. The study, focused on the Spanish Electricity Market as a representative example, combines different training criteria and different users of the forecasts to compare them in terms of the benefits obtained. In addition to more classical criteria, an information theoretic learning training criterion, called parametric correntropy, is introduced as a means to correct problems detected in other criteria and achieve more satisfactory compromises among conflicting criteria, namely forecasting value and quality. We show that the interests of wind farm owners may lead to a preference for biased forecasts, which may be in conflict with the larger needs of secure operating policies. The ideas and conclusions are supported by results from three real wind farms. Copyright © 2010 John Wiley & Sons, Ltd.

[1]  Vladimiro Miranda,et al.  Wind power forecasting in U.S. Electricity markets , 2010 .

[2]  R. Barthelmie,et al.  The economic benefit of short-term forecasting for wind energy in the UK electricity market , 2008 .

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

[4]  M. O'Malley,et al.  System operation with a significant wind power penetration , 2004, IEEE Power Engineering Society General Meeting, 2004..

[5]  John Bjørnar Bremnes,et al.  Probabilistic wind power forecasts using local quantile regression , 2004 .

[6]  Ulfar Linnet,et al.  Tools supporting wind energy trade in deregulated markets , 2005 .

[7]  A. H. Murphy,et al.  Value of Weather Information: A Descriptive Study of the Fruit-Frost Problem. , 1984 .

[8]  Jian-Wu Xu,et al.  Nonlinear Signal Processing Based on Reproducing Kernel Hilbert Space , 2010 .

[9]  Pierre Pinson,et al.  Estimation of the uncertainty in wind power forecasting , 2006 .

[10]  A. H. Murphy,et al.  A General Framework for Forecast Verification , 1987 .

[11]  A. H. Murphy,et al.  Quality value relationships for imperfect weather forecasts in a prototype multistage decision-making model , 1990 .

[12]  A. Fabbri,et al.  Assessment of the cost associated with wind generation prediction errors in a liberalized electricity market , 2005, IEEE Transactions on Power Systems.

[13]  V. Miranda,et al.  Entropy and Correntropy Against Minimum Square Error in Offline and Online Three-Day Ahead Wind Power Forecasting , 2009, IEEE Transactions on Power Systems.

[14]  Weifeng Liu,et al.  Correntropy: Properties and Applications in Non-Gaussian Signal Processing , 2007, IEEE Transactions on Signal Processing.

[15]  H. Holttinen.  Impact of hourly wind power variations on the system operation in the Nordic countries , 2005 .

[16]  Pierre Pinson,et al.  Standardizing the Performance Evaluation of Short-Term Wind Power Prediction Models , 2005 .

[17]  Deniz Erdogmus,et al.  Generalized information potential criterion for adaptive system training , 2002, IEEE Trans. Neural Networks.

[18]  P. Pinson,et al.  Trading Wind Generation From Short-Term Probabilistic Forecasts of Wind Power , 2007, IEEE Transactions on Power Systems.

[19]  Julio Usaola-García,et al.  Analysis of a wind farm's revenue in the British and Spanish markets , 2007 .

[20]  Hans Bludszuweit,et al.  Reduction of the uncertainty of wind power predictions using energy storage , 2009 .

[21]  Manuel A. Matos,et al.  Operating reserve adequacy evaluation using uncertainties of wind power forecast , 2009, 2009 IEEE Bucharest PowerTech.

[22]  Georges Kariniotakis,et al.  Advanced strategies for wind power trading in short-term electricity markets , 2008 .

[23]  M. O'Malley,et al.  A new approach to quantify reserve demand in systems with significant installed wind capacity , 2005, IEEE Transactions on Power Systems.

[24]  Nicholas R. Jennings,et al.  Using similarity criteria to make issue trade-offs in automated negotiations , 2002, Artif. Intell..

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

[26]  D. Hawkins,et al.  Operations of Electric Power Systems with High Penetration of Wind Power: Risks and Possible Solutions , 2007, 2007 IEEE Power Engineering Society General Meeting.

[27]  E. Damme.  Stability and perfection of Nash equilibria , 1987 .

[28]  Deniz Erdogmus,et al.  Information Theoretic Learning , 2005, Encyclopedia of Artificial Intelligence.

[29]  Ronnie Belmans,et al.  Development of balancing in the internal electricity market in Europe , 2006 .

[30]  Vladimiro Miranda,et al.  Risk management and optimal bidding for a wind power producer , 2010, IEEE PES General Meeting.

[31]  Henrik Madsen,et al.  A review on the young history of the wind power short-term prediction , 2008 .

[32]  H.F. Ravn.  Short term wind power prognosis with different success criteria , 2006, 2006 International Conference on Probabilistic Methods Applied to Power Systems.

[33]  I. Erlich,et al.  European Balancing Act , 2007, IEEE Power and Energy Magazine.