Improvement and automation of tools for short term wind power forecasting

European Wind Energy Conference and Exhibition 2007, EWEC 2007

Volumn 1, Issue , 2007, Pages 539-548

  Nielsen, Henrik Aalborg ^a   Pinson, Pierre ^a   Christiansen, Lasse Engbo ^a   Nielsen, Torben Skov ^c   Madsen, Henrik ^a   Badger, Jake ^a   Giebel, Gregor ^a   Ravn, Hans F ^b  

^a TECHNICAL UNIVERSITY OF DENMARK   (Denmark)

^b RAM lose edb   (Denmark)

^c ENFOR A/S   (Denmark)

Author keywords

Robustness; Software; Wind power forecasting

Indexed keywords

AUTOMATIC SELECTION; FORECAST SYSTEMS; ROBUST ESTIMATION; SHORT-TERM WIND POWER FORECAST; SHORT-TERM WIND POWER FORECASTING; TUNING PARAMETER; WIND POWER FORECASTING; WIND POWER PREDICTIONS;

COMPUTER SOFTWARE; EXHIBITIONS; ROBUSTNESS (CONTROL SYSTEMS); WIND POWER;

WEATHER FORECASTING;

EID: 84875356226     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper

References (27)

1. Adrian G. and Fiedler F. Simulations of unstationary wind and temperature fields over complex terrrain and comparion with observations. Beitr. Phy. Atmosph., 64:27-48, 1991.
2. Bates J.M. and Granger C.W.J. The combination of forecasts. Operational Research Quarterly, 20(4):451-468, 1969.
3. Christiansen L.E., Nielsen H.A., Nielsen T.S., and Madsen H. Automatic selection of tuning parameters in wind power prediction. Technical report, Informatics and Mathematical Modelling, Technical University of Denmark, 2007. To be published.
4. Cleveland W. Multivariate Analysis and Its Applications,, chapter Coplots, Nonparametric Regression, and conditionally Parametric Fits, pp. 21-36. Institute of Mathematical Statistics, Hayward, 1994.
5. de Menezes L.M. and Taylor J. W. Review of guidelines for the use of combined forecasts. European Journal of Operational Research, 120(1): 190-204, 2000.
6. Frank H.R and Landberg L. Modelling the wind climate of ireland. Boundary-Layer Meteorology, 85:359- 378, 1997.
7. Frey-Buness F., Heimann D., and Sausen R. A statistical-dynamical downscaling procedure for global climate simulations. Theor. Appl. Climatol., 50:117-131, 1995.
8. Giebel G., Brownsword R., and Kariniotakis G. The state-of-the-art in short-term prediction of wind power; a literature overview. Technical report, Project ANEMOS public document, 2003. http://anemos.cma.fr/download/ANEMOS-Dl.l- StateOfTheArt-vl.1.pdf.
9. Granger C.W.J. and Ramanathan R. Improved methods of combining forecasts. Journal of Forecasting, 3:197-204, 1984.
10. Haykin S. Adaptive Filter Theory. Prentice Hall, 3rd edition, 1996.
11. Huber P.J. Robust Statistics. Wiley, New York, 1981.
12. Ljung L. System Identification, Theory for the User. Prentice-Hall, Englewood Cliffs, New Jersey, 1987.
13. Ljung L. and Gunnarsson S. Adaption and tracking in system identification - a survey. Automatica, 26:7- 22, 1990.
14. Madsen H., Nielsen H.A., and Nielsen T.S. A tool for predicting the wind power production of off-shore wind plants. In Proceedings of the Copenhagen Offshore Wind Conference & Exhibition. Danish Wind Industry Association, Copenhagen, 2005a. http://www.windpower.org/en/core.htm.
15. Madsen H., Pinson P., Kariniotakis G., Nielsen H.A., and Nielsen T.S. Standardizing the performance evaluation of short-term wind prediction models. Wind Engineering, 29(6):475-489, 2005b. URL http://www2.imm.dtu.dk/pubdb/p.php? 4262.
16. Nielsen H. A., Madsen H., and Nielsen T.S. Using quan- tile regression to extend an existing wind power forecasting system with probabilistic forecasts. Wind Energy, 9:95-108, 2006a.
17. Nielsen H.A., Nielsen T.S., Joensen A.K., Madsen H., and Hoist J. Tracking time-varying coefficient- functions. Int. J. of Adaptive Control and Signal Processing, 14(8):813-828, 2000a.
18. Nielsen H.A., Nielsen T.S., and Madsen H. On on-line systems for short-term forecasting for energy systems. In Proceedings of the OR 2002 conference, pp. 265-271. Springer, Klagenfurt, Austria, 2002a.
19. Nielsen H. A., Nielsen T.S., and Madsen H. Using meteorological forecasts for short term wind power forecasting. In Proceedings of the IEA R&D Wind Annex XI Joint Action Symposium on Wind Forecasting Techniques, pp. 49 -58. Norrkoping, Sweden, 2002b.
20. Nielsen H.A., Nielsen T.S., Madsen H., Giebel G., Badger J., Landberg L., Sattler K., Voulund L., and Tøfting J. From wind ensembles to probabilistic information about future wind power production - results from an actual application. In Proceedings of the 9th International Conference on Probabilistic Methods Applied to Power Systems. 2006b. Invited paper.
21. Nielsen H.A., Nielsen T.S., Madsen H., Pindado M J.S.I., and Marti I. Optimal combination of wind power forecasts. Wind Energy, 2007. To be published.
22. Nielsen T., Madsen H., and Christensen H. WPPT - a tool for wind power prediction. In Proceedings of the Wind Power Jor the 21st Century Conference. Kassel, Germany, 2000b.
23. Nielsen T., Madsen H., and Christensen H.S. WPPT - a tool for wind power prediction. In Proceedings of the Wind Power for the 21st Century Conference. Kassel, 2000c.
24. Pinson P., Nielsen H., Moller J., Madsen H., and Kariniotakis G. Probabilistic forecasts of wind power: required properties and evaluation. Wind Energy, 2006. Submitted.
25. Pinson P. and Nielsen H.A. Robust estimation of time- varying coefficient functions - Application to the modelling of wind power production. Technical report, Technical University of Denamrk, Informatics and Mathematical Modelling, 2007. PSO project FU4101. (available online: www2.imm.dtu.dk/~-pp).
26. Sanchez I. Adaptive combination of forecast with application to wind energy forecast. In 9th International Conference on Probabilistic Methods Applied to Power Systems. Power Engineering Society, IEEE, KTH, Stockholm, Sweden, 2006.
27. Sejling K., Madsen H., Hoist J., Hoist U., and Englund J. Methods for robust estimation of ar-parameters. Computational Statistics and Data Analysis, 17:509- 536, 1994.