An advanced bayesian method for short-term probabilistic forecasting of the generation of wind power

Energies

Volumn 8, Issue 9, 2015, Pages 10293-10314

  Bracale, Antonio ^a   De Falco, Pasquale ^b  

^a UNIVERSITY OF NAPLES PARTHENOPE   (Italy)

^b UNIVERSITY OF NAPLES FEDERICO II   (Italy)

Author keywords

Forecasting methods; Power production; Probabilistic approaches; Wind energy

Indexed keywords

BAYESIAN NETWORKS; ELECTRIC POWER GENERATION; ELECTRIC POWER SYSTEMS; INFERENCE ENGINES; MIXTURES; WEIBULL DISTRIBUTION; WIND EFFECTS; WIND POWER;

FORECASTING METHODS; FORECASTING PERFORMANCE; INTERMITTENT NATURE OF WINDS; MIXTURE WEIBULL DISTRIBUTIONS; POWER PRODUCTION; PROBABILISTIC APPROACHES; PROBABILISTIC FORECASTING; RENEWABLE DISTRIBUTED GENERATIONS;

WEATHER FORECASTING;

EID: 84945268824     PISSN: None     EISSN: 19961073     Source Type: Journal    
DOI: 10.3390/en80910293     Document Type: Article

References (60)

1. Fang, X.; Misra, S.; Xue, G.; Yang, D. Smart grid-The new and improved power grid: A survey. IEEE Commun. Surv. Tutor. 2012, 14, 944-980.
2. Farhangi, H. The path of the smart grid. IEEE Power Energy Mag. 2010, 8, 18-28.
3. Blumsack, S.; Fernandez, A. Ready or not, here comes the smart grid! Energy 2012, 37, 61-68.
4. Lo, C.H.; Ansari, N. The progressive smart grid system from both power and communications aspects. IEEE Commun. Surv. Tutor. 2012, 14, 799-821.
5. Zhou, L.; Rodrigues, J.J.P.C. Service-oriented middleware for smart grid: Principle, infrastructure, and application. IEEE Commun. Mag. 2013, 51, 84-89.
6. Zhou, L.; Rodrigues, J.J.P.C.; Oliveira, L.M. QoE-driven power scheduling in smart grid: Architecture, strategy, and methodology. IEEE Commun. Mag. 2012, 50, 136-141.
7. Santacana, E.; Rackliffe, G.; Tang, L.; Feng, X. Getting smart. IEEE Power Energy Mag. 2010, 8, 41-48.
8. CIGRE-International Council on Large Electrical Systems. Impact of Increasing Contribution of Dispersed Generation on the Power System; CIGRE WG 37-23; CIGRE: Paris, France, 1999.
9. Smith, J.C. Winds of change. IEEE Power Energy 2005, 3, 20-24.
10. Bessa, R.J.; Miranda, V.; Botterud, A.; Wang, J. Good or bad wind power forecasts: A relative concept. Wind Energy 2011, 14, 625-636.
11. Potter, C.W.; Archambault, A.; Westrick, K. Building A Smarter Smart Grid through Better Renewable Energy Information. In Proceedings of the IEEE/PES Power Systems Conference and Exposition, Seattle, WA, USA, 15-18 March 2009.
12. 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. Renew. Sustain. Energy Rev. 2008, 12, 1725-1744.
13. Pinson, P.; Nielsen, H.; Møller, J.K.; Madsen, H.; Kariniotakis, G.N. Non-parametric probabilistic forecasts of wind power: Required properties and evaluation. Wind Energy 2007, 10, 497-516.
14. Pinson, P.; Juban, J.; Kariniotakis, G.N. On The Quality and Value of Probabilistic Forecasts of Wind Generation. In Proceedings of the International Conference on Probabilistic Methods Applied to Power Systems, Stockholm, Sweden, 11-15 June 2006.
15. Kariniotakis, G.N.; Pinson, P.; Siebert, N.; Giebel, G.; Bartelmie, R. State of the art in short-term prediction of wind power-From an offshore perspective. In Proceedings of Sea Tech Week, Brest, France, 20-21 October 2004.
16. Bracale, A.; Caramia, P.; Carpinelli, G.; Di Fazio, A.R.; Ferruzzi, G. A Bayesian method for short-term probabilistic forecasting of photovoltaic generation in smart grid operation and control. Energies 2013, 6, 733-747.
17. Li, G.; Shi, J. Applications of Bayesian methods in wind energy conversion systems. Renew. Energy 2013, 43, 1-8.
18. Uusitalo, L. Advantages and challenges of Bayesian networks in environmental modelling. Ecol. Model. 2007, 203, 3-4.
19. Miranda, M.S.; Dunn, R.W. One-Hour-Ahead Wind Speed Prediction using a Bayesian methodology. In Proceedings of the IEEE Power Engineering Society General Meeting, Montreal, QC, Canada, 18-22 June 2006.
20. Jiang, Y.; Song, Z.; Kusiak, A. Very short-term wind speed forecasting with Bayesian structural break model. Renew. Energy 2013, 50, 637-647.
21. Kim, C.J.; Kim, J. Bayesian inference in regime-switching arma models with absorbing states: The Dynamics of the ex-ante real interest rate under regime shifts. J. Bus. Econ. Stat. 2014, doi:10.1080/07350015.2014.979995.
22. Gelman, A.; Carlin, J.B.; Stern, H.S.; Rubin, D.B. Bayesian Data Analysis; Chapman & Hall: London, UK, 1995.
23. Zhang, J.; Pu, J.; McCalley, J.D.; Stern, H.; Gallus, W.A., Jr. A Bayesian approach for short-term transmission line thermal overload risk assessment. IEEE Trans. Power Deliv. 2002, 17, 770-778.
24. Bracale, A.; Caramia, P.; Carpinelli, G.; Di Fazio, A.R.; Varilone, P. A Bayesian-based approach for a short-term steady-state forecast of a smart grid. IEEE Trans. Smart Grid 2013, 4, 1760-1771.
25. Carta, J.A.; Ramirez, P.; Velazquez, S. A review of wind speed probability distributions used in wind energy analysis. Case studies in the Canary islands. Renew. Sustain. Energy Rev. 2009, 13, 933-955.
26. Box, G.; Jenkins, G. Time Series Analysis: Forecasting and Control, 3rd ed.; Prentice-Hall: Upper Saddle River, NJ, USA, 1994.
27. Xie, L.; Gu, Y.; Zhu, X.; Genton, M.G. Short-term spazio-temporal wind power forecast in robust look-ahead power system dispatch. IEEE Trans. Smart Grid 2014, 5, 511-520.
28. Black, J. Plans for Wind Integration in ISO-NE: Progress and Challenges. In Proceedings of the 8th Annual Carnegie Mellon Conference on Electricity Industry, Pittsburgh, PA, USA, 12-14 March 2012; pp. 1-37.
29. Deep Thunder-Precision Forecasting for Weather-Sensitive Business Operations. 2012. Available online: http://www.research.ibm.com/weather/DT.html (accessed on 15 September 2015).
30. Mahoney, W.P.; Parks, K.; Wiener, G.; Liu, Y.; Myers, W.L.; Sun, J.; Delle Monache, L.; Hopson, T.; Johnson, D.; Haupt, S.E. A wind power forecasting system to optimize grid integration. IEEE Trans. Sustain. Energy 2012, 3, 670-682.
31. Constantinescu, E.M.; Zavala, V.M.; Rocklin, M.; Lee, S.; Anitescu, M. A computational framework for uncertainty quantification and stochastic optimization in unit commitment with wind power generation. IEEE Trans. Power Syst. 2011, 26, 431-441.
32. Genton, M.; Hering, A. Blowing in the wind. Significance 2007, 4, 11-14.
33. Giebel, G.; Brownsword, R.; 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 Project Deliverable Report D1.2. 2011. Available online: http://orbit.dtu.dk/fedora/objects/orbit:83397/datastreams/file_5277161/content (accessed on 15 September 2015).
34. Karaki, S.H.; Chedid, R.B.; Ramadan, R. probabilistic performance assessment of wind energy conversion systems. IEEE Trans. Energy Convers. 1999, 14, 217-224.
35. Taylor, J.W.; McSharry, P.E.; Buizza, R. Wind power density forecasting using ensemble predictions and time series models. IEEE Trans. Energy Convers. 2009, 24, 775-782.
36. Hering, A.S.; Genton, M. Powering up with space-time wind forecasting. J. Am. Stat. Assoc. 2009, 105, 92-104.
37. Gneiting, T. Quantiles as optimal point forecasts. Int. J. Forecast. 2011, 27, 197-207.
38. Sánchez, I. Short-term prediction of wind energy production. Int. J. Forecast. 2006, 22, 43-56.
39. Jeon, J.; Taylor, J.W. Using conditional kernel density estimation for wind power density forecasting. J. Am. Stat. Assoc. 2012, 107, 66-79.
40. Lange, M. On the uncertainty of wind power predictions-Analysis of the forecast accuracy and statistical distribution of errors. J. Solar Energy Eng. 2005, 127, 177-184.
41. Korpaas, M.; Holen, A.T.; Hildrum, R. Operation and sizing of energy storage for wind power plants in a market system. Int. J. Electr. Power Energy Syst. 2003, 25, 599-606.
42. Matevosyan, J.; Söder, L. Minimization of imbalance cost trading wind power on the short-term power market. IEEE Trans. Power Syst. 2006, 21, 1396-1404.
43. Morales, J.M.; Conejo, A.J.; Pérez-Ruiz, J. Short-term trading for a wind power producer. IEEE Trans. Power Syst. 2010, 25, 554-564.
44. Chang, T.P.; Liu, F.J.; Ko, H.; Cheng, S.; Sun, L.; Kuo, S. Comparative analysis on power curve models of wind turbine generator in estimating capacity factor. Energy 2014, 73, 88-95.
45. Titterington, D.M.; Smith, A.F.M.; Makov, U.E. Statistical Analysis of Finite Mixture Distributions, 2nd ed.; Wiley: New York, NY, USA, 1995.
46. Kaylan, A.R.; Harris, C.M. Efficient algorithms to derive maximum-likelihood estimates for finite exponential and weibull mixtures. Comput. Oper. Res. 1981, 8, 97-104.
47. Gamerman, D. Markov Chain Monte Carlo: Stochastic Simulation for Bayesian Inference; Chapman & Hall: London, UK, 1997.
48. Carta, J.A.; Ramirez, P. Analysis of two-component mixture Weibull statistics for estimation of wind speed distributions. Renew. Energy 2007, 32, 518-531.
49. Martz, H.F.; Waller, R.A. Bayesian Reliability Analysis; John Wiley and Sons: New York, NY, USA, 1982.
50. Berger, J.O. Statistical decision theory and Bayesian analysis; Springer Science & Business Media: New York, NY, USA, 2013.
51. Von Neumann, J. Various Techniques Used in Connection With Random Digits; National Bureau of Standards Applied Mathematics Series; U.S. Government Printing Office: Washington, DC, USA, 1951; Volume 12, pp. 36-38.
52. Gneiting, T.; Raftery, A.E.; Westveld, A.H., III; Goldman, T. calibrated probabilistic forecasting using ensemble model output statistics and minimum CRPS estimation. Mon. Weather Rev. 2005, 133, 1098-1118.
53. Gneiting, T.; Raftery, A.E. Strictly proper scoring rules, prediction, and estimation. J. Am. Stat. Assoc. 2007, 102, 359-378.
54. Pinson, P.; Chevallier, C.; Kariniotakis, G. Trading wind generation from short-term probabilistic forecasts of wind power. IEEE Trans. Power Syst. 2007, 22, 1148-1156.
55. Bacher, P.; Madsen, H.; Nielsen, H.A. Online short-term solar power forecasting. Solar Energy 2009, 83, 1772-1783.
56. Pinson, P.; Reikard, G.; Bidlot, J.R. Probabilistic forecasting of the wave energy flux. Appl. Energy 2012, 93, 364-370.
57. Pinson, P. Very-short-term probabilistic forecasting of wind power with generalized logit-normal distributions. J. R. Stat. Soc. 2012, 61, 555-576.
58. Czado, C.; Gneiting, T.; Held, L. Predictive model assessment for count data. Biometrics 2009, 65, 1254-1261.
59. Diebold, F.X.; Gunther, T.A.; Tay, A.S. Evaluating density forecasts with applications to financial risk management. Int. Econ. Rev. 1998, 39, 863-883.
60. Gneiting, T.; Balabdaoui, F.; Raftery, A.E. Probabilistic forecasts, calibration and sharpness. J. R. Stat. Soc. 2007, 69, 243-268.