A method for short-term wind power prediction with multiple observation points

IEEE Transactions on Power Systems

Volumn 27, Issue 2, 2012, Pages 579-586

  Khalid, Muhammad ^a   Savkin, Andrey V ^a  

^a UNIVERSITY OF NEW SOUTH WALES   (Australia)

Author keywords

Adaptive filtering; networked systems; prediction; renewable energy; wind power

Indexed keywords

AUSTRALIA; GREY PREDICTOR; MEAN ABSOLUTE ERROR; NETWORKED SYSTEMS; NUMERICAL WEATHER PREDICTION; OBSERVATION POINT; OUTPUT POWER; POWER CURVES; REAL MEASUREMENTS; RENEWABLE ENERGIES; ROOT MEAN SQUARE ERRORS; WIND FARM; WIND POWER PREDICTIONS; WIND SPEED;

ADAPTIVE FILTERING; ELECTRIC UTILITIES; FORECASTING; MEAN SQUARE ERROR; TURBINES; WEATHER FORECASTING; WIND EFFECTS;

WIND POWER;

EID: 84860254202     PISSN: 08858950     EISSN: None     Source Type: Journal    
DOI: 10.1109/TPWRS.2011.2160295     Document Type: Article

References (24)

1. S. Breton G. Moe Status, plans and technologies for offshore wind turbines in Europe and North America Renew. Energy 34 3 646-654 2009.
2. M. Milligan, A. Miller, and F. Chapman, " Estimating the economic value of wind forecasting to utilities," in Proc. American Wind Energy Association Conf. , 1995, pp. 285-294, 72.
3. R. J. Barthelmie, F. Murray, and S. C. Pryor, " The economic benefit of short-term forecasting for wind energy in the UK electricity market, " Energy Policy , vol. 36, no. 5, pp. 1687-1696, 2008.
4. J. Usaola, O. Ravelo, G. Gonzáles, F. Soto, M. C. Dávila, and B. Díz- Guerra, " Benefits for wind energy in electricity markets from using short term wind power prediction tools: A simulation study," Wind Eng. , vol. 28, no. 1, pp. 119-128, 2004. (Pubitemid 38762738)
5. A. S. Matveev and A. V. Savkin, " The problem of state estimation via asynchronous communication channels with irregular transmission times, " IEEE Trans. Autom. Control , vol. 48, no. 4, pp. 670-676, Apr. 2003.
6. A. V. Savkin, " Analysis and synthesis of networked control systems: Topological entropy, observability, robustness, and optimal control, " Automatica, vol. 42, no. 1, pp. 51-62, Jan. 2006. (Pubitemid 41660561)
7. A. S. Matveev and A. V. Savkin, Estimation and Control Over Communication Networks . Boston, MA: Birkhauser, 2009.
8. Y. Hirata, D. P. Mandic, H. Suzuki, and K. Aihara, " Wind direction modelling using multiple observation points," Philosoph. Trans. Roy. Soc. A: Math., Phys. Eng. Sci. , vol. 366, no. 1865, pp. 591-607, 2008.
9. M. C. Alexiadis, P. S. Dokopoulos, H. S. Sahsamanoglou, and I. M. Manousaridis, "Short-term forecasting of wind speed and related electrical power, " Solar Energy, vol. 63, no. 1, pp. 61-68, 1998. (Pubitemid 28397933)
10. G. N. Bathurst, J. Weatherill, and G. Strbac, " Trading wind generation in short-term energy markets," IEEE Trans. Power Syst. , vol. 17, no. 3, pp. 782-789, Aug. 2002. (Pubitemid 35151758)
11. N. Cutler,M. Kay, H. Outhred, and I. MacGill, " High-risk scenarios for wind power forecasting in Australia," in Proc. European Wind Energy Conf. Exhib. , Italy, May 2007.
12. E. A. Bossanyi, " Short-term wind prediction using Kalman filters," Wind Eng. , vol. 9, no. 1, pp. 1-8, 1985. (Pubitemid 15543356)
13. H. Fukuda et al. , " The development of a wind velocity prediction method based on a data-mining type auto-regressive model, " in Proc. European Wind Energy Conf. , Copenhagen, Denmark, Jun. 2-6, 2001, pp. 741-744.
14. J. Torres, A. García, M. D. Blas, and A. D. Francisco, " Forecast of hourly average wind speed with ARMA models in navarre (Spain), " Solar Energy , vol. 79, no. 1, pp. 65-77, Jul. 2005.
15. G. N. Kariniotakis, G. S. Stavrakakis, and E. F. Nogaret, " Wind power forecasting using advanced neural networks models, " IEEE Trans. Energy Convers. , vol. 11, no. 4, pp. 762-767, Dec. 1996. (Pubitemid 126771432)
16. T. H. M. El-Fouly, E. F. El-Saadany, and M. M. A. Salama, " Grey predictor for wind energy conversion systems output power production, " IEEE Trans. Power Syst. , vol. 21, no. 3, pp. 1450-1452, Aug. 2006. (Pubitemid 44263540)
17. C. W. Potter and M. Negnevitsky, " Very short-term wind forecasting for tasmanian power generation, " IEEE Trans. Power Syst. , vol. 21, no. 2, pp. 965-972, May 2006.
18. C. Monteiro, R. Bessa, V. Miranda, A. Botterud, J. Wang, and G. Conzelmann,Wind Power Forecasting: State-of-the-Art 2009 Argonne National Laboratory, a report, 2009, ANL/DIS-10-1.
19. A. Costa A. Crespo J. Navarro G. Lizcano H. Madsen E. Feitosa "A review on the young history of the wind power short-term prediction Renew. Sustain. Energy Rev. 12 6 1725-1744 2008. (Pubitemid 351626911)
20. K. Puri, G. S. Dietachmayer,G. A. Mills, N. E. Davidson, R. A. Bowen, and L. W. Logan, " The new BMRC limited area prediction system, LAPS," Australian Meteorol. Mag. , vol. 47, no. 3, pp. 203-223, 1998. (Pubitemid 128184055)
21. S. L. Goh, M. Chen, D. H. Popovic, K. Aihara, D. Obradovic, and D. P. Mandic, " Complex-valued forecasting of wind profile," Renew. Energy , vol. 31, no. 11, pp. 1733-1750, 2006. (Pubitemid 44108627)
22. I. Sanchez, " Short-term prediction of wind energy production," Int. J. Forecast. , vol. 22, pp. 43-56, 2006. (Pubitemid 43174790)
23. S. Li, D. C. Wunsch, E. A. O'Hair, and M. G. Giesselmann, "Using neural networks to estimate wind turbine power generation, " IEEE Trans. Energy Convers., vol. 16, no. 3, pp. 276-282, Sep. 2001. (Pubitemid 32780121)
24. D. Cabezon, I. Marti, M. J. S. Isidro, and I. Perez , Comparison of Methods for Power Curve Modeling. Chicago, IL: Global Windpower, 2004.