Forecasting ramps of wind power production with numerical weather prediction ensembles

Wind Energy

Volumn 16, Issue 1, 2013, Pages 51-63

  Bossavy, Arthur ^a   Girard, Robin ^a   Kariniotakis, George ^a  

^a PSL RESEARCH UNIVERSITY   (France)

Author keywords

extreme situations; numerical weather prediction ensembles; ramp event; renewable energies; wind power forecasting

Indexed keywords

EDGE DETECTION; ELECTRIC UTILITIES; INFORMATION MANAGEMENT; WIND POWER;

EXTREME SITUATIONS; NUMERICAL WEATHER PREDICTION; RAMP EVENTS; RENEWABLE ENERGIES; WIND POWER FORECASTING;

WEATHER FORECASTING;

ENSEMBLE FORECASTING; FILTRATION; NUMERICAL MODEL; PREDICTION; RENEWABLE RESOURCE; RISK FACTOR; WEATHER FORECASTING; WIND POWER;

FRANCE;

EID: 84872946665     PISSN: 10954244     EISSN: 10991824     Source Type: Journal    
DOI: 10.1002/we.526     Document Type: Article

References (48)

1. Doherty R, O'Malley M,. A new approach to quantify reserve demand in systems with significant installed wind capacity. Power Systems, IEEE Transactions on 2005; 20 (2): 587-595.
2. Castronuovo ED, Lopes JAP,. On the optimization of the daily operation of a wind-hydro power plant. Power Systems, IEEE Transactions on 2004; 19 (3): 1599-1606.
3. Angarita JM, Usaola JG,. Combining hydro-generation and wind energy: biddings and operation on electricity spot markets. Electric Power Systems Research 2007; 77 (5-6): 393-400.
4. Pinson P, Chevallier C, Kariniotakis G,. Trading wind generation from short-term probabilistic forecasts of wind power. Power Systems, IEEE Transactions on 2007; 22 (3): 1148-1156.
5. Bremnes BJ,. A comparison of a few statistical models for making quantile wind power forecasts. Wind Energy 2006; 9 (1): 3-11.
6. Moller JK, Nielsen HAa, Madsen H,. Time-adaptive quantile regression. Computational Statistics & Data Analysis 2008; 52 (3): 1292-1303.
7. Nielsen HAa, Madsen H, Nielsen TS,. Using quantile regression to extend an existing wind power forecasting system with probabilistic forecasts. Wind Energy 2006; 9 (1-2): 95-108.
8. Juban J, Siebert N, Kariniotakis G,. Probabilistic short-term wind power forecasting for the optimal management of wind generation, Proceedings of the IEEE Power Tech Conference, Lausanne, Swizerland, 2007.
9. Taylor JW, McSharry PE, Buizza R,. Wind power density forecasting using ensemble predictions and time series models. Energy Conversion, IEEE Transactions on 2009; 24 (3): 775-782.
10. Costa A, Crespo A, Navarro J, Lizcano G, Madsen H, Feitosa E,. A review on the young history of the wind power short-term prediction. Renewable and Sustainable Energy Reviews 2008; 12 (6): 1725-1744.
11. Giebel G, Kariniotakis G, Brownsword R,. The state of the art on short-term wind power prediction-a literature overview. Technical report, ANEMOS EU project, deliverable report D1.1, 2003. [Available online:http://www.anemos- project.eu].
12. Pinson P,. Catalogue of complex to extreme situations. Technical report, EU Project SafeWind, Deliverable Dc1.2, 2009. [Available online: http://www.safewind.eu].
13. Cutler NJ, Kay M, Jacka K, Nielsen TS,. Detecting, categorizing and forecasting large ramps in wind farm power output using meteorological observations and WPPT. Wind Energy 2007; 10 (5): 453-470.
14. Cutler NJ,. Characterizing the uncertainty in potential large rapid changes in wind power generation, PhD Thesis, Electrical Engineering & Telecommunications, Faculty of Engineering, UNSW, 2009. [Available online: http://handle.unsw.edu.au/1959.4/43570].
15. Zheng H, Kusiak A,. Prediction of wind farm power ramp rates: a data-mining approach. Journal of solar energy engineering 2009; 131: 031011.1-031011.8.
16. Zareipour H,. Wind power ramp events classification and forecasting: a data mining approach, Proceedings of the 2011 IEEE Power and Energy Society (PES) Annual General Meeting, Detroit, USA, 2011.
17. Kamath C,. Associating weather conditions with ramp events in wind power generation, IEEE PES Power Systems Conference & Exposition, Phoenix, Arizona, 2011.
18. Cutler NJ, Outhred HR, MacGill IF, Kay MJ, Kepert JD,. Characterizing future large, rapid changes in aggregated wind power using numerical weather prediction spatial fields. Wind Energy 2009; 12 (6): 542-555.
19. Greaves B, Collins J, Parkes J, Tindal A,. Temporal forecast uncertainty for ramp events. Wind Engineering 2009; 33 (11): 309-319.
20. Ferreira C, Gama J, Matias L, Botterud A, Wang J,. A survey on wind power ramp forecasting, A report from the Argonne US Department of Energy Laboratory, 2010. [Available online at http://www.dis.anl.gov/].
21. Pinson P, Kariniotakis G,. Conditional prediction intervals of wind power generation. Power Systems, IEEE Transactions on 2010; 25 (4): 1845-1856.
22. Nielsen HAa, Nielsen T, Madsen H, Badger J, Giebel G, Landberg L, Sattler K, Voulund L, Tøfting J,. From wind ensembles to probabilistic information about future wind power production-results from an actual application, Proceedings of the IEEE PMAPS 2006 Conference, Probabilistic Methods Applied to Power Systems, Stockholm, Sweden, 2006.
23. Pinson P, Madsen H,. Ensemble-based probabilistic forecasting at Horns Rev. Wind Energy 2009; 12 (2): 137-155.
24. Chessa P, Lalaurette F,. Verification of the ECMWF ensemble prediction system forecasts: a study of large-scale patterns. Weather and Forecasting 2001; 16 (5): 611-619.
25. WEPROG ApS. HRensembleHR-High resolution ensemble for Horns Rev, Technical Report, Project funded by the Danish PSO F&U Program, Final Report, 2010. [Available online: http://www.hrensemble.net].
26. Grimit E, Potter C,. A prototype day-ahead forecast system for rapid wind ramp events, Proceedings of Windpower 2008 Conference and Exhibition, Houston, Texas, 2008.
27. Girard R, Pinson P,. Evaluation of time trajectories-application to wind power forecasting. Applied Energy 2011. in press.
28. Ziou D, Tabbone S,. Edge detection techniques-an overview. International Journal of Pattern Recognition and Image Analysis 1998; 8: 537-559.
29. Torre V, Poggio TA,. On edge detection. Pattern Analysis and Machine Intelligence, IEEE Transactions on 1986; PAMI-8 (2): 147-163.
30. Canny J,. A computational approach to edge detection. Pattern Analysis and Machine Intelligence, IEEE Transactions on 1986; PAMI-8 (6): 679-698.
31. Demigny D,. On optimal linear filtering for edge detection. Image Processing, IEEE Transactions on 2002; 11 (7): 728-737.
32. Pinson P,. Estimation of the uncertainty in wind power forecasting, PhD Thesis, MINES Paris Tech, 2006. [Available online: http://pastel.paristech.org].
33. Fan J, Gijbels I,. Variable bandwidth and local linear regression smoothers. Annals of Statistics 1992; 20: 2008-2036.
34. Hastie T, Tibshirani R, Friedman J,. The elements of statistical learning: data mining, inference, and prediction, 2nd edn, Springer Series in Statistics. Springer, 2009.
35. Schucany W,. Kernel smoothers: an overview of curve estimators for the first graduate course in nonparametric statistics. Statistical Sciences 2004; 19: 663-675.
36. Madsen H, Thyregod P,. An Introduction to General and Generalized Linear Models. Chapman & Hall, 2010.
37. Eccel E, Ghielmi L, Granitto P, Barbiero R, Grazzini F, Cesari D,. Prediction of minimum temperatures in an alpine region by linear and non-linear post-processing of meteorological models. Nonlinear Processes in Geophysics 2007; 14 (3): 211-222.
38. Davy R, Woods M, Russell C, Coppin P,. Statistical downscaling of wind variability from meteorological fields. Boundary-Layer Meteorology 2010; 135: 161-175.
39. Breiman L,. Random Forests. Machine Learning 2001; 45: 5-32.
40. Kariniotakis G, Marti I, Casas D, Pinson P, Nielsen T, Madsen H, Giebel G, Usaola J, Sanchez I, Palomares AM, Brownsword R, Tambke J, Focken U, Lange M, Loucka P, Kallos G, Lac C, Sideratos G, Descombes G,. What performances can be expected by short-term wind power prediction models depending on site characteristics? Proceedings of the 2004 European Wind Energy Conference EWEC'04, London, UK, 2004.
41. Brier G,. Verification of forecast expressed in terms of probability. Monthly weather review 1950; 78: 1-3.
42. Bradley A, Schwartz SS, Hashino T,. Sampling uncertainty and confidence intervals for the Brier score and Brier skill score. Weather and Forecasting 2008; 23 (5): 992-1006.
43. Gneiting T, Balabdaoui F, Raftery AE,. Probabilistic forecasts, calibration and sharpness. Journal of the Royal Statistical Society: Series B (Statistical Methodology) 2007; 69 (2): 243-268.
44. Pinson P, Nielsen HAa, Moller JK, Madsen H, Kariniotakis G,. Non-parametric probabilistic forecasts of wind power: required properties and evaluation. Wind Energy 2007; 10 (6): 497-516.
45. Broecker J, Smith LA,. Increasing the reliability of reliability diagrams. Weather and Forecasting 2007; 22 (3): 651-661.
46. Hong Y,. On computing the distribution function for the sum of independent and non-identical random indicators, Technical Report, Department of Statitics, Virginia Tech, Blacksburg, VA, 2011.
47. Murphy AH,. A new vector partition of the probability score. Journal of Applied Meteorology 1973; 12: 595-600.
48. Pinson P, Madsen H, Nielsen HAa, Papaefthymiou G, Klöckl B,. From probabilistic forecasts to statistical scenarios of short-term wind power production. Wind Energy 2009; 12 (1): 51-62.