Reviews on uncertainty analysis of wind power forecasting

Renewable and Sustainable Energy Reviews

Volumn 52, Issue , 2015, Pages 1322-1330

  Yan, Jie ^a   Liu, Yongqian ^a   Han, Shuang ^a   Wang, Yimei ^a   Feng, Shuanglei ^b  

^a NORTH CHINA ELECTRIC POWER UNIVERSITY   (China)

^b CHINA ELECTRIC POWER RESEARCH INSTITUTE   (China)

Author keywords

Classification; Impact factors; Interval forecasting; Optimization strategy; Probabilistic forecasting; Uncertainty analysis; Wind power forecasting

Indexed keywords

DECISION MAKING; WEATHER FORECASTING; WIND POWER;

CONFIDENCE LEVELS; DECISION MAKERS; FORECASTING MODELS; IMPACT FACTOR; INTERVAL FORECASTING; OPTIMIZATION STRATEGY; POWER SYSTEM OPERATIONS; PROBABILISTIC FORECASTING; PROBABILISTICS; WIND POWER FORECASTING;

UNCERTAINTY ANALYSIS;

EID: 84940390709     PISSN: 13640321     EISSN: 18790690     Source Type: Journal    
DOI: 10.1016/j.rser.2015.07.197     Document Type: Review

References (76)

1. G. Giebel, G. Kariniotakis, R. Brownsword, The state of the art in short-term prediction of wind power - from a Danish perspective. In Proceedings of the 4th international workshop on large-scale integration of wind power and transmission networks for offshore wind farms. Billund, Denmark, October 20-21; 2003.
2. J. Juban, L. Fugon, G. Kariniotakis, Uncertainty estimation of wind power forecasts: comparison of probabilistic modelling approaches. European wind energy conference & exhibition EWEC 2008. Brussels: Belgium; 2008.
3. G. Giebel, R. Brownsword, G. Kariniotakis State of the art on short-term wind power prediction. ANEMOS Deliverable Report D1.1, available online: (http://anemos.cma.fr), June; 2003.
4. M. Lange Analysis of the uncertainty of wind power predictions [Ph.D. dissertation] 2003 Carl von Ossietzky Oldenburg University Wuppertal
5. P. Pinson, C. Chevallier, and G. Kariniotakis Trading wind generation from short-term probabilistic forecasts of wind power IEEE Trans Power Syst 22 3 2007 1148 1156
6. E.D. Castronuovo, and J.A.P. Lopes On the optimization of the daily operation of a wind-hydro power plant IEEE Trans Power Syst 19 3 2004 1599 1606
7. P. Pinson, G. Papaefthymiou, B. Klöckl, HA. Nielsen, Generation of statistical scenarios of short-term wind power production. Power Tech, 2007 IEEE. Lausanne, 1-5 July; 2007.
8. P. Pinson, J.K. Møller, H.A. Nielsen, and H. Madsen Evaluation of nonparametric probabilistic forecasts of wind power [Informatics and mathematical modeling technical report IMM-2007-02] 16 January 2007 Technical University of Denmark
9. C. Mohrlen Uncertainty in wind energy forecasting [Ph.D dissertation] 2004 University College Cork, National University of Ireland Moehrlen
10. M. Lange On the uncertainty of wind power predictions-Analysis of the forecast accuracy and statistical distribution of errors J Sol Energy Eng (Trans ASME) 127 2 2005 177 184
11. P. Pinson Estimation of the uncertainty in wind power forecasting 2006 Ecole des Mines de Paris France
12. P. Pinson, and G. Karinioiotis Conditional prediction intervals of wind power generation IEEE Trans Power Syst 25 4 2010
13. J. Yan Research on uncertainty analysis method for wind power prediction and its application 2012 North China Electric Power University Beijing, [. . :,2012]
14. J.K. Møller, H.A. Nielsen, and H. Madsen Time-adaptive quantile regression Comp Stat Data Anal 52 3 2008 1292 1303
15. P. Pinson, HA. Nielsen, H. Madsen, Methods for the Estimation of the Uncertainty of Wind Power Forecasts. March 23; 2007
16. P. Pinson, H.A. Nielsen, H. Madsen, and G. Kariniotakis Skill forecasting from ensemble predictions of wind power Appl Energy 86 2009 1326 1334
17. A. Costa, A. Crespo, J. Navarro, G. Lizcano, H. Madsen, and E. Feitona A review on the young history of wind power short-term prediction Renew Sustain Energy Rev 12 2008 1725 1744
18. P. Pinson, H.A. Nielsen, J.K. Møller, H. Madsen, and G. Kariniotakis Nonparametric probabilistic forecasts of wind power: required properties and evaluation Wind Energy 10 2007 497 516
19. Marti, ANEMOS deliverable report D2.1. Available online: (http://anemos.cma.fr); 2004.
20. G. Giebel Wind power prediction using ensembles technical report [Risø-R-1527(EN)] 2005
21. C. Chatfield The analysis of time-series: an introduction 6th ed. 2003 Chapman & Hall/CRC London, U.K.
22. G.F. Fan, W.S. Wang, C. Liu, and H.Z. Dai Wind power prediction based on artificial neural network Proc CSEE 28 34 2008 118 123
23. P.A. da Silva, and L.S. Moulin Confidence intervals for neural network based short term load forecasting IEEE Trans Power Syst 15 4 Nov. 2000 1191 1196
24. C. Chatfield Calculating interval forecasts J Bus Econ. Stat 11 1993 121 135
25. P. Pinson Estimation of the uncertainty in wind power forecasting 2006 Ecole des Mines de Paris Paris, France
26. W.H. Williams, and M.L. Goodman A simple method for the construction of empirical confidence limits for economic forecasts J Am Stat Assoc 66 336 1971 752 754
27. H.A. Nielsen, H. Madsen, and T.S. Nielsen Using quantile regression to extend an existing wind power forecasting system with probabilistic forecasts Wind Energy 9 1-2 2006 95 108
28. P. Pinson, L.E.A. Christensen, H. Madsen, P.E. Sørensen, M.H. Donovan, and L.E. Jensen Regime-switching modelling of the fluctuations of offshore wind generation J. Wind Eng Ind Aerodyn 96 12 2008 2327 2347
29. P. Pinson, H. Madsen. Probabilistic forecasting of wind power at the minute time-scale with Markov-switching autoregressive models.
30. J.W. Taylor, and D.W. Bunn Investigating improvements in the accuracy of prediction intervals for combination of forecasts: a simulation study Int J Forecast 19 1 1999 57 70
31. J.B. Bremnes Probabilistic wind power forecasts using local quantile regression Wind Energy 7 1 2004 47 54
32. J.B. Bremnes A comparison of a few statistical models for making quantile wind power forecasts Wind Energy 9 2006 3 11
33. J. Taylor, and P. Mcsharry Wind Power Density Forecasting Using Ensemble Predictions and Time Series Models Energy Conversion IEEE Transaction on 24 3 2009 775 782
34. J.K. Sivillo, J.E. Ahlquist, and Z. Toth. An ensemble forecasting primer Weather Forecast 12 4 1997 809 818
35. Y.Q. Liu, J. Yan, S. Han, Y.H. Peng, Uncertainty analysis of wind power prediction based on quantile regression[C]. The 4th IEEE Asia-Pacific power and energy engineering conference (APPEEC 2012). Shanghai, China, March 27-29; 2012
36. J. Juban, N. Siebert, GN. Kariniotakis, Probabilistic Shortterm Wind Power Forecasting for the optimal management of wind generation, Power Tech, IEEE LausanneIEEE, 2007, 683-688
37. R. Bessa, J. Mendes, V. Miranda, A. Botterud, J.B. Wang, Z. Zhou, Quantile-copula density forecast for wind power uncertainty modeling, PowerTech, 2011 IEEE Trondheim, 19-23 June 2011, Trondheim, 1-8.
38. R. Bessa, V. Miranda, A. Botterud, Z. Zhou, and J. Wang Time-adaptive quantile-Copula for wind power probabilistic forecasting Renew Energy 40 1 2012 2 7
39. B. Enfron, and R.J. Tibshirani An introduction to the bootstrap 2007 Chapman & Hall/CRC London, U.K.
40. T.L. Thorarinsdottir, and T. Gneiting Probabilistic forecasts of wind speed: ensemble model output statistics by using heteroscedastic censored regression J R Stat Soc A 173 2010 371 388
41. P. Pinson, and H. Madsen Ensemble-based probabilistic forecasting at Horns Rev Wind Energy 12 2 2009 137 155
42. P. Pinson, and G. Kariniotakis On-line assessment of prediction risk for wind power production forecasts Wind Energy 7 2004 119 132 10.1002/we.114
43. P. Pinson, and H. Madsen Adaptive modeling and forecasting of wind power fluctuations with Markov-switching autoregressive models [Technical report No. IMM-2009-11] 2009 Technical University of Denmark, DTU Informatics Kgs. Lyngby, Denmark
44. A.R. Hendersen, C. Morgan, B. Smith, H.C. Sørensen, R.J. Barthelmie, and B. Boesmans Offshore wind energy in Europe - a review of the state-of-the-art Wind Energy 6 2003 35 52
45. P. Sørensen, N. Cutululis, A. Vigueras-Rodriguez, H. Madsen, P. Pinson, L. Jensen, J. Hjerrild, and M. Donovan Modelling of power fluctuations from large offshore wind farms Wind Energy 11 1 2008 29 43
46. H.A. Nielsen, T.S. Nielsen, H. Madsen, G. Giebel, J. Badger, L. Landbergt, From wind ensembles to probabilistic information about future wind power production - results from an actual application. Proc. IEEE PMAPS conference, probabilistic methods applied to power systems. Stockholm, Sweden.
47. G. Giebel, G. Kariniortakis, R. Brownsword, The state of the art in short-term prediction of wind power - a literature overview. Technical report, ANEMOS EU project, deliverable report D1.1; 2003. [Available online: (http://www.anemos-project.eu) ]
48. A. Costa, A. Crespo, J. Navarro, G. Lizcano, H. Madsen, and E. Feitosa A review on the young history of the wind power short-term prediction Renew Sustain Energy Rev 12 2008 1725 1744
49. P.E. Sørensen, N.A. Cutululis, A. Vigueras-Rodriguez, L.E. Jensen, J. Hjerrild, M.H. Donovan, and H. Madsen Power fluctuations from large wind farms IEEE Trans Power Syst 22 2007 958 965
50. P. Pinson, On probabilistic forecasting of wind power time-series. (http://forskningsbasen.deff.dk/Share.external?sp=S753df533-9ea1-4577-a27e-1f8149b05825&sp=Sdtu)
51. A. Luig, S. Bofinger, HG. Beyer, Analysis of confidence intervals for the prediction of the regional wind power output. Proceedings of the European wind energy conference. Copenhagen; 2001.
52. J.D. Hamilton A new approach to the economic analysis of nonstationary time-series and business cycles Econometrica 5.7 1989 357 384
53. T. Gneiting, K. Larson, K. Westrick, M.G. Genton, and E. Aldrich Calibrated probabilistic forecasting at the state-line wind energy center - the regime-switching space-time method J Am Stat Assoc 101 2006 968 979
54. I. Sanchez Short-term prediction of wind energy production Int J Forecast 22 2006 43 56
55. C.L. Vincent, G. Giebel, P. Pinson, and H. Madsen Resolving non-stationary spectral signals in wind speed time-series using the Hilbert-Huang transform J Appl Meteorol Climatol 49 2010 253 267
56. P. Pinson, G. Papaefthymiou, B. Klöckl, H.A. Nielsen, and H. Madsen From probabilistic forecasts to statistical scenarios of short-term wind power production Wind Energy 12 1 2009 51 62
57. G. Papaefthymiou and P. Pinson, Modeling of spatial dependence in wind power forecast uncertainty, Probabilistic Methods Applied to Power Systems.pmaps. preoceedings of Interm, 2008, 1-9.
58. G. Papaefthymiou Integration of stochastic generation in power systems 2007 Delft University of Technology Delft, The Netherlands [Ph.D. dissertation]
59. T.R. Stewart, Uncertainty, judgement, and error in prediction, Prediction: Science, decision making, and the future of nature, 2000, 41-47
60. J. Yan, Y.Q. Liu, and S. Han Application of quantile regression on wind power prediction uncertainty analysis Acta Energiae Sol Sin 34 12 2013 2101 2107, in Chinese[[J].. 2013, 34 (12): 2101-2107]
61. L. Li, Y.Q. Liu, Y.P. Yang, and S. Han Short-term wind speed forecasting based on CFD pre-calculated flow fields Proc CSEE 33 7 2013 27 32
62. J. Morales, L. Baringo, A. Concjo, and R. Minguez Probabilistic power flow with correlated wind sources IET Gener Transm Distrib 4 5 2010 641 651
63. Qiang Fu, D. Yu, J. Ghorai, Probabilistic load flow analysis for power systems with multi-correlated wind sources. Power and energy society general meeting, IEEE, 24-29 July 2011, San Diego, CA; 2011. p. 1-6.
64. Y. Degeilh, and C. Singh A quantitative approach to wind farm diversification and reliability Electr Power Energy Syst 33 2011 303 314
65. P. Pinson, G. Papaefthymiou, B. Klockl, H.A. Nielsen, and H. Madsen From probabilistic forecasts to short-term statistical scenarios of wind generation Wind Energy 2015 [in press]
66. H. Nielsen, T. Nielsen, H. Madsen, G. Giebel, J. Badger, L.Landbergt, From wind ensembles to probabilistic information about future wind power production - results from an actual application. Proc. IEEE PMAPS conference, probabilistic methods applied to power systems. Stockholm, Sweden.
67. J. Wang, A. Botterud, R. Bessa, H. Keko, L. Carvalho, D. Issicaba, J. Sumaili, and V. Miranda Wind power forecasting uncertainty and unit commitment Appl Energy 88 2011 4014 4023
68. J. Yan, Y.Q. Liu, S. Han, and M. Qiu Wind power grouping forecasts and its uncertainty analysis using optimized relevance vector machine Renew Sustain Energy Rev 27 2013 613 621
69. J. Yan, Y.Q. Liu, S. Han, Y. Yang, An integration of enhanced wind power interval forecasting into reactive power dispatching. IET 2nd renewable power generation conference. Beijing China, Sep 9-11; 2013.
70. Y. Zhang, J. Wang, and X. Wang Review on probabilistic forecasting of wind power generation Renew Sustain Energy Rev 32 2014 255 270
71. A.U. Haque, M.H. Nehrir, and P. Mandal Hybrid intelligent model for deterministic and quantile regression approach for probabilistic wind power forecasting IEEE Trans Power Syst 29 4 2014 1663 1672
72. G. Sideratos, and N.D. Hatziargyriou Hatziargyriou, Probabilistic wind power forecasting using radial basis function neural networks IEEE Trans Power Syst 27 4 2012 1788 1796
73. W. Can, X. Zhao, P. Pinson, Y. Zhao, and P. Kit Probabilistic forecasting of wind power generation using extreme learning machine IEEE Trans Power Syst 29 3 2014 1033 1044
74. W. Can, X. Zhao, P. Pison, Y. Zhao, and P. Kit Optimal prediction intervals of wind power generation IEEE Trans Power Syst 29 3 2014 1166 1174
75. G. Huang, Q. Zhu, Y. Qin, and C. Siew Extreme learning machine: theory and applications Neurocomputing 70 1 2006 489 501
76. P. Kou, D. Liang, F. Gao, and L. Gao Probabilistic wind power forecasting with online model selection and warped gaussian process Energy Convers Manag 84 2014 649 663