Day-ahead wind speed forecasting using f-ARIMA models

Renewable Energy

Volumn 34, Issue 5, 2009, Pages 1388-1393

  Kavasseri, Rajesh G ^a   Seetharaman, Krithika ^a  

^a North Dakota State University   (United States)

Author keywords

f ARIMA models; Time series analysis; Wind speed forecasting

Indexed keywords

ELECTRIC GENERATORS; ELECTRIC LOAD FORECASTING; FORECASTING; HURRICANE EFFECTS; RENEWABLE ENERGY RESOURCES; SPEED; TIME SERIES; TIME SERIES ANALYSIS; WIND EFFECTS;

DAY-AHEAD ELECTRICITY MARKETS; ELECTRICITY GRIDS; F-ARIMA MODELS; FORECAST ERRORS; FORECASTING ACCURACIES; NORTH DAKOTAS; PERSISTENCE MODELS; POWER CURVES; WIND ENERGIES; WIND GENERATIONS; WIND POWER PRODUCTIONS; WIND SPEED; WIND SPEED FORECASTING; WIND SPEED FORECASTS;

WIND POWER;

ACCURACY ASSESSMENT; ELECTRICITY GENERATION; ELECTRICITY INDUSTRY; FORECASTING METHOD; REGRESSION ANALYSIS; TIME SERIES; WIND POWER; WIND VELOCITY;

NORTH AMERICA; NORTH DAKOTA; UNITED STATES;

EID: 58949103845     PISSN: 09601481     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.renene.2008.09.006     Document Type: Article

References (24)

1. U.S. Federal Energy Regulatory Commission (FERC). Assessing the state of wind energy in wholesale electricity markets: comments of Basin Electric Power Co-operative. FERC Docket No. AD04-13-000; 2005.
2. Box G.E.P., and Jenkins G.M. Time series analysis: forecasting and control. revised ed. (1976), Holden-Day, San Francisco
3. Corotis R.N., Sigl A.B., and Cohen P.M. Variance analysis of wind characteristics for energy conversion. Journal of Applied Meteorology 16 (Nov 1977) 1149-1157
4. Daniel A.R., and Chen A.A. Stochastic simulation and forecasting of hourly average wind speed sequences in Jamaica. Solar Energy 46 1 (1991) 1-11
5. Nielsen T.L., Madsen H., and Tofting J. Experience with statistical methods for wind power prediction. Proceedings of the European wind energy conversion (1999) 1066-1069 Nice, France
6. Milligan M, Schwartz M, Wan Y. Statistical wind power forecasting models: results for U.S. wind farms. NREL-CP-500-33956; May 2003. http://www.nrel.gov/docs/fy03osti/33956.pdf.
7. Bossanyi E.A. Short term stochastic prediction and possible control applications. Proceedings of the Delphi workshop on wind energy applications, Greece (May 1985) 66-79
8. Kariniotakis G.N., Stavrakakis G.S., and Nogaret E.F. Wind power forecasting using advanced neural networks. IEEE Transactions on Energy Conversion 11 4 (Dec 1996) 762-767
9. Kariniotakis G.N., Nogaret E.F., and Stavrakakis G.S. Advanced short-term forecasting of wind power production. Proceedings of the European wind energy conference (1997) 751-754
10. Alexiadis M.C., Dokopoulus P.S., and Sahsamanoglou H. Wind speed and power forecasting based on spatial correlation models. IEEE Transactions on Energy Conversion 14 3 (Sep 1999) 836-842
11. Kavasseri R.G., and Nagarajan R. Evidence of crossover phenomena in wind speed data. IEEE Transactions on Circuits and Systems 51 11 (Nov 2004) 2255-2262
12. Kavasseri R.G., and Nagarajan R. A multifractal description of wind speed records. Chaos, Solitons and Fractals 24 1 (2005) 165-173
13. Contreras J., Espinola R., Nogales F.J., and Conejo A.J. ARIMA models to predict next day electricity prices. IEEE Transactions on Power Systems 18 3 (Aug 2003) 1014-1020
14. Sowell F. Maximum likelihood estimation of stationary univariate fractionally integrated models. Journal of Econometrics 53 (1992) 165-188
15. Fox R., and Taqqu M. Large sample properties of parameter estimates for strongly dependent stationary Gaussian time series. Annals of Statistics 14 (1986) 517-532
16. Dahlhaus R. Efficient parameter estimation for self similar processes. Annals of Statistics 17 (1989) 1749-1766
17. Beran J. Statistics for long memory processes (1994), Chapman and Hall, New York
18. Doornik J.A. Ox: an object oriented matrix language (2001), Timberlakes Consultants Press, London
19. Doornik J.A., and Ooms M. Computational aspects of maximum likelihood estimation of autoregressive fractionally integrated moving average models. Computational Statistics and Data Analysis 41 (2003) 333-348
20. Cheung Y.W., and Diebold F.X. On maximum likelihood estimation of the differencing parameter of fractionally integrated noise with unknown mean. Journal of Econometrics 62 (1994) 301-316
21. Brockwell P.J., and Davis R.A. Time series: theory and methods (1991), Springer-Verlag, New York
22. Akaike H. Information theory and extension of the maximum likelihood principle. In: Second international symposium of information theory, Budapest. 1973. p. 267-81.
23. Panofsky H.A., and Dutton J.A. Atmospheric turbulence (1984), Wiley, New York
24. Freris L.L. Wind energy conversion systems (1990), Prentice Hall, New Jersey