Short-term forecasting of categorical changes in wind power with Markov chain models

Wind Energy

Volumn 17, Issue 9, 2014, Pages 1425-1439

  Yoder, Megan ^a   Hering, Amanda S ^a   Navidi, William C ^a   Larson, Kristin ^b  

^a Department of Geology and Geological Engineering   (United States)

^b Global Weather Corporation   (United States)

Author keywords

forecast evaluation; Markov chain models; multinomial proportions confidence intervals; wind power

Indexed keywords

MARKOV CHAINS; WIND; WIND POWER;

CONFIDENCE INTERVAL; FORECAST EVALUATION; MARKOV CHAIN MODELS; METEOROLOGICAL STATION; RENEWABLE WIND ENERGY; SHORT TERM PREDICTION; SHORT-TERM FORECASTING; WIND SPEED FORECASTING;

WEATHER FORECASTING;

ELECTRICITY; ELECTRICITY GENERATION; MARKOV CHAIN; WIND POWER; WIND VELOCITY;

PACIFIC NORTHWEST;

EID: 84905867413     PISSN: 10954244     EISSN: 10991824     Source Type: Journal    
DOI: 10.1002/we.1641     Document Type: Article

References (44)

1. Botterud A, Wang J, Miranda V, Bessa RJ,. Wind power forecasting in U.S. electricity markets. Electricity Journal 2010; 23: 71-82.
2. Marquis M, Wilczak J, Ahlstrom M, Sharp J, Stern A, Smith JC, Calvert S,. Forecasting the wind to reach significant penetration levels of wind energy. Bulletin of the American Meteorological Society 2011; 92: 1159-1171.
3. Kiviluoma J, OMalley M, Tuohy A, Meibom P, Milligan M, Lange B, Holttinen H, Gibescu M,. 2011. Impact of wind power on the unit commitment, operating reserves, and market design. In IEEE/PES General Meeting, Detroit, MI.
4. Lannoye E, Flynn D, OMalley MJ,. 2011. The role of power system flexibility in generation planning. In IEEE/PES General Meeting, 2011, Detroit, MI.
5. Ortega-Vazquez M, Kirschen D,. Estimating the spinning reserve requirements in systems with significant wind power generation penetration. IEEE Transactions on Power Systems 2009; 24: 114-123.
6. Delucchi M, Jacobson M,. Providing all global energy with wind, water, and solar power. Part II: reliability, system and transmission costs, and policies. Energy Policy 2011; 39: 1170-1190.
7. Potter C, Grimit E, Nijssen B,. 2009. Potential benefits of a dedicated probabilistic rapid ramp event forecast tool. In IEEE/PES Power Systems Conference, Seattle, Washington.
8. Genton MG, Hering AS,. Blowing in the wind. Significance 2007; 4: 11-14.
9. Giebel G, Brownsword B, Kariniotakis G, Denhard M., Draxl C,. The State-Of-The-Art in Short-Term Prediction of Wind Power: A Literature Overview, 2nd ed., ANEMOS.plus: 2011.
10. Haupt SE, Wiener G, Liu Y, Myers B, Sun J, Johnson D, Mahoney W,. 2011. A wind power forecasting system to optimize power integration. In Proceedings of the ASME 5th International Conference on Energy Sustainability, Washington, D.C.
11. Sloughter JM, Gneiting T, Raftery AE,. Probabilistic wind speed forecasting using ensembles and bayesian model averaging. Journal of the American Statistical Association 2010; 105: 25-35.
12. Taylor JW, McSharry PE, Buizza R,. Wind power density forecasting using ensemble predictions and time series. IEEE Transactions on Energy Conversion 2009; 24: 775-782.
13. Williams, JL III, Maxwell RM,. Propagating subsurface uncertainty to the atmosphere using fully-coupled, stochastic simulations. Journal of Hydrometeorology 2011; 12: 690-701.
14. de Luna X, Genton MG,. Predictive spatio-temporal models for spatially sparse environmental data. Statistica Sinica 2005; 15: 547-568. (Pubitemid 40816420)
15. Gneiting T, Larson K, Westrick K, Genton MG, Aldrich E,. Calibrated probabilistic forecasting at the stateline wind energy center: the regime-switching space-time method. Journal of the American Statistical Association 2006; 101: 968-979. (Pubitemid 44422598)
16. Hering AS, Genton MG,. Powering up with space-time wind forecasting. Journal of the American Statistical Association 2010; 105: 92-104.
17. Jeon J, Taylor JW,. Using conditional kernel density estimation for wind power density forecasting. Journal of the American Statistician 2012; 107: 66-79.
18. Juban J, Fugon L, Kariniotakis G,. 2007. Probabilistic short-term wind power forecasting based on kernel density estimators. In European Wind Energy Conference, Milan.
19. Pinson P, Madsen H,. Adaptive modelling and forecasting of offshore wind power fluctuations with Markov-switching autoregressive models. Journal of Forecasting 2012; 31: 281-313.
20. Zhu X, Genton MG,. Short term wind speed forecasting for power system operations. International Statistical Review 2012; 80: 2-23.
21. Bossavy A, Girard R, Kariniotakis G,. Forecasting wind power uncertainty related to ramp events. In European Wind Energy Conference, Warsaw 2010.
22. Ferreira C, Gama J, Matias L, Botterud A, Wang J,. A survey on wind power ramp forecasting, (2010). Available at http://www.dis.anl.gov/pubs/69166.pdf Accessed 05/28/13.
23. Kamath C,. 2010. Understanding wind ramp events through analysis of historical data. In IEEE/PES Transmission and Distribution Conference, New Orleans, LA.
24. Bremnes JB,. Probabilistic wind power forecasts using local quantile regression. Wind Energy 2004; 7: 47-54. (Pubitemid 40290428)
25. Gneiting T,. Quantiles as optimal point forecasts. International Journal of Forecasting 2011; 27: 197-207.
26. Sahin AD, Sen Z,. First-order Markov chain approach to wind speed modeling. Journal of Wind Engineering and Industrial Aerodynamics 2001; 89: 263-269. (Pubitemid 32235786)
27. Youcef Ettoumi F, Sauvageot H, Adane AEH,. Statistical bivariate modelling of wind using first-order Markov chain and Weibull distribution. Renewable Energy 2003; 28: 1787-1802. (Pubitemid 36463666)
28. Shamshad A, Bawadi MA, Wan Hussin WMA, Majid TA, Sanusi SAM,. First and second order Markov chain models for synthetic generation of wind speed time series. Energy 2005; 30: 693-708. (Pubitemid 39333086)
29. Negra NB, Holmstrøm O., Bak-Jensen B., Sørensen P,. Model of a synthetic wind speed time series generator. Wind Energy 2008; 11: 193-209. (Pubitemid 351542804)
30. Hocaoglu FO, Gerek ON, Kurban M,. 2008. The effect of Markov chain state size for synthetic wind speed generation. In Probabilistic Methods Applied to Power Systems. Proceedings of the 10th International Conference, Puerto Rico.
31. Brokish K, Kirtley J,. 2009. Pitfalls of modeling wind power using Markov chains. In IEEE/PES Power Systems Conference and Exposition, Seattle, WA.
32. Mardia KV, Jupp PE,. Directional Statistics, John Wiley Sons, Ltd: New York, NY, 2000.
33. Archer CL, Jacobson MZ,. Spatial and temporal distributions of U.S. winds and wind power at 80 m derived from measurements. Journal of Geophysical Research 2003; 108: 1-20.
34. Gneiting T, Raftery AE,. Strictly proper scoring rules, prediction, and estimation. Journal of the American Statistical Association 2007; 102: 359-378.
35. Larson KA, Westrick K,. Short-term wind forecasting using off-site observations. Wind Energy 2006; 9: 55-62. (Pubitemid 43281984)
36. Fitzpatrick S, Scott A,. Quick simultaneous confidence intervals for multinomial proportions. Journal of the American Statistical Association 1987; 82: 875-878.
37. Glaz J, Sison CP,. Simultaneous confidence intervals for multinomial proportions. Journal of Statistical Planning and Inference 1999; 82: 251-262.
38. Hou C, Chiang J, Tai JJ,. A family of simultaneous confidence intervals for multinomial proportions. Computational Statistics and Data Analysis 2003; 43: 29-45. (Pubitemid 36568264)
39. Sison CP, Glaz J,. Simultaneous confidence intervals and sample size determination for multinomial proportions. Journal of the American Statistical Association 1995; 90: 366-369.
40. Pinson P, Nielsen HA, Madsen H, Nielsen TS,. Local linear regression with adaptive orthogonal fitting for the wind power application. Statistics and Computing 2008; 18: 59-71.
41. Breiman L, Friedman J, Stone CJ, Olshen RA,. Classification and Regression Trees, CRC Press: Boca Raton, FL, 1984.
42. Loh W,. Classification and regression trees. Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery 2011; 1: 14-23.
43. Davis GM, Ensor KB,. Multivariate times-series analysis with categorical and continuous variables in an LSTR model. Journal of Time Series Analysis 2007; 28: 867-885.
44. Fokianos K, Kedem B,. Regression theory for categorical time series. Statistical Science 2003; 18: 357-376. (Pubitemid 38457916)