Predicting wind power variability events using different statistical methods driven by regional atmospheric model output

Wind Energy

Volumn 18, Issue 9, 2015, Pages 1611-1628

  Ellis, Nick ^a   Davy, Robert ^a   Troccoli, Alberto ^a  

^a CSIRO   (Australia)

Author keywords

classification; forecasting; gradient boosting; machine learning; numerical weather prediction; random forests; reliability; ROC; support vector machines

Indexed keywords

ADAPTIVE BOOSTING; CLASSIFICATION (OF INFORMATION); DECISION TREES; ELECTRIC POWER SYSTEM INTERCONNECTION; ELECTRIC UTILITIES; FORECASTING; LEARNING SYSTEMS; ORTHOGONAL FUNCTIONS; RANDOM FORESTS; RELIABILITY; SUPPORT VECTOR MACHINES; WIND; WIND POWER;

EMPIRICAL ORTHOGONAL FUNCTION; GRADIENT BOOSTING; MISCLASSIFICATION RATES; NATIONAL CENTERS FOR ENVIRONMENTAL PREDICTIONS; NUMERICAL WEATHER PREDICTION; RECEIVER OPERATING CHARACTERISTIC CURVES; SUPPORT VECTOR MACHINE CLASSIFICATION; WIND POWER VARIABILITIES;

WEATHER FORECASTING;

EID: 84939792593     PISSN: 10954244     EISSN: 10991824     Source Type: Journal    
DOI: 10.1002/we.1779     Document Type: Article

References (45)

1. Danish Energy Agency. Monthly Energy Statistics, summary table. http://www.ens.dk/sites/ens.dk/files/dokumenter/side/el-maanedsstatistik.xls (Accessed September 2013)
2. EnerNex Corporation. Eastern Wind Integration and Transmission Study (EWITS) (Revised), 2011; 242. http://www.nrel.gov/docs/fy11osti/47078.pdf (Accessed September 2013)
3. Australian Energy Market Operator. 2011 National Transmission Network Development Plan, Australian Energy Market Operator, 2011. http://www.aemo.com.au/Reports-and-Documents/DevelopmentPlans/~/media/Files/Other/planning/ntndp/NTNDP2011-CD/documents/NTNDP-2011%20pdf.ashx. (Accessed September 2013)
4. Tande JOG,. Grid integration of wind farms. Wind Energy 2003; 6: 281-295. DOI: 10.1002/we.91
5. Ela E, Kirby B,. ERCOT Event on February 26, 2008: lessons learned, national renewable energy laboratory 1617 Cole Blvd. Golden, CO 80401-3393, Golden, CO, USA, 2008. http://www.nrel.gov/docs/fy08osti/43373.pdf. (Accessed September 2013)
6. Foley AM, Leahy PG, Marvuglia A, McKeogh EJ,. Current methods and advances in forecasting of wind power generation. Renewable Energy 2012; 37: 1-8. DOI: 10.1016/j.renene.2011.05.033.
7. Louka P, Galanis G, Siebert N, Kariniotakis G, Katsafados P, Pytharoulis I, Kallos G,. Improvements in wind speed forecasts for wind power prediction purposes using Kalman filtering. Journal of Wind Engineering and Industrial Aerodynamics 2008; 96: 2348-2362.
8. Focken U, Lange M, Mönnich K, Waldl HP, Beyer HG, Luig A,. Short-term prediction of the aggregated power output of wind farms-a statistical analysis of the reduction of the prediction error by spatial smoothing effects. Journal of Wind Engineering and Industrial Aerodynamics 2002; 90: 231-246.
9. Taylor JW, Buizza R,. Using weather ensemble predictions in electricity demand forecasting. International Journal of Forecasting 2003; 19: 57-70.
10. Cutler N, Outhred H, MacGill I,. Final report on UNSW Project for AEMO to develop a prototype wind power forecasting tool for potential large rapid changes in wind power, Australian Energy Market Operator, 2011. http://www.aemo.com.au/Electricity/Market-Operations/Dispatch/AWEFS/~/media/Files/Other/electricityops/0269-0001%20pdf.ashx. (Accessed September 2013)
11. Bossavy A, Girard R, Kariniotakis G,. Forecasting ramps of wind power production with numerical weather prediction ensembles. Wind Energy 2013; 16: 51-63. DOI: 10.1002/we.526.
12. Cutler NJ, Outhred HR, MacGill IF, Kay MJ, Kepert JD,. Characterizing future large, rapid changes in aggregated wind power using numerical weather prediction spatial fields. Wind Energy 2009; 12: 542-555. DOI: 10.1002/we.312.
13. Greaves B, Collins J, Parkes J, Tindal A,. Temporal forecast uncertainty for ramp events. Wind Engineering 2009; 33: 309-319. DOI: 10.1260/030952409789685681.
14. Davy RJ, Woods MJ, Russell CJ, Coppin PA,. Statistical downscaling of wind variability from meteorological fields. Boundary-Layer Meteorology 2010; 135: 161-175. DOI: 10.1007/s10546-009-9462-7.
15. Vincent CL, Pinson P, Giebel G,. Wind fluctuations over the North Sea. International Journal of Climatology 2011; 31: 1584-1595. DOI: 10.1002/joc.2175.
16. Von Bremen L, Saleck N,. Minimizing the risk in offshore wind power integration induced by severe wind power fluctuations. In The Science of Making Torque from Wind. European Academy of Wind Energy: Crete, 2010.
17. Vincent CL, Giebel G, Russell CJ, Woods MJ, Davy RJ,. Documentation of the severity index for wind variability and its application to wind power forecasting, SafeWind Collaborative project funded by the European Commission under the 7th Framework Program, Theme 2007-2.3.2: Energy, 2011. http://www.safewind.eu/images/Articles/Deliverables/swind-deliverable-dp-4.7-severityindexes-v1.0.pdf. (Accessed September 2013)
18. Australian Energy Market Operator. http://www.nemweb.com.au/REPORTS/CURRENT/Next-Day-Actual-Gen. (Accessed September 2013)
19. Sanchez I,. Short-term prediction of wind energy production. International Journal of Forecasting 2006; 22: 43-56. DOI: 10.1016/j.ijforecast.2005.05.003.
20. Anastasiades G, McSharry P,. Quantile forecasting of wind power using variability indices. Energies 2013; 6: 662-695.
21. Jolliffe IT,. Uncertainty and Inference for verification measures. Weather and Forecasting 2007; 22: 637-650. DOI: 10.1175/WAF989.1.
22. von Storch H, Zwiers FW,. Statistical analysis in climate research. Cambridge University Press: Cambridge, 2001.
23. Richman MB,. Rotation of principal components. Journal of Climatology 1986; 6: 293-335. DOI: 10.1002/joc.3370060305.
24. Mason SJ, Baddour O,. Statistical modelling. In Seasonal Climate: Forecasting and Managing Risk, Troccoli A, Harrison M, Anderson DLT, Mason SJ, (eds). Springer: Netherlands, 2008; 163-201.
25. Nelder JA, Wedderburn RWM,. Generalized linear models. Journal of the Royal Statistical Society. Series A (General) 1972; 135: 370-384. DOI: 10.2307/2344614.
26. R Development Core Team. R: A Language and Environment for Statistical Computing, R Foundation for Statistical Computing, Vienna, Austria, 2011. http://www.R-project.org/. (Accessed September 2013)
27. Schmeits MJ, Kok KJ,. A comparison between raw ensemble output, (modified) Bayesian model averaging, and extended logistic regression using ECMWF ensemble precipitation reforecasts. Monthly Weather Review 2010; 138: 4199-4211.
28. Breiman L,. Random forests. Machine Learning 2001; 45: 5-32. DOI: 10.1023/A:1010933404324.
29. Breiman L,. Bagging predictors. Machine Learning 1996; 24: 123-140. DOI: 10.1007/BF00058655.
30. Segal MR,. Machine learning benchmarks and random forest regression. University of California, 2004. http://www.escholarship.org/uc/item/35x3v9t4. (Accessed September 2013)
31. Statnikov A, Wang L, Aliferis CF,. A comprehensive comparison of random forests and support vector machines for microarray-based cancer classification. BMC Bioinformatics 2008; 9. DOI: 10.1186/1471-2105-9-319.
32. Friedman JH,. Greedy function approximation: a gradient boosting machine. Annals of Statistics 2001; 29: 1189-1232.
33. Cortes C, Vapnik V,. Support-vector networks. Machine Learning 1995; 20: 273-297. DOI: 10.1007/BF00994018.
34. Bishop CM,. Pattern Recognition and Machine Learning. Springer: New York, 2006.
35. Chang C-C, Lin C-J,. LIBSVM: A library for support vector machines. ACM Transactions on Intelligent Systems and Technology 2011; 2: 1-27. DOI: 10.1145/1961189.1961199.
36. Shi J, Yang Y, Wang P, Liu Y, Han S,. Genetic algorithm-piecewise support vector machine model for short term wind power prediction. In 8th World Congress on Intelligent Control and Automation. Jinan, China, 2010; 2254-2258.
37. Akaike H,. A new look at the statistical model identification. IEEE Transactions on Automatic Control 1974; 19: 716-723. DOI: 10.1109/TAC.1974.1100705.
38. Burnham KP, Anderson DR,. Model selection and multi-model inference: a practical information-theoretic approach. Springer: New York, 2002.
39. Hastie T, Tibshirani R, Friedman JH,. The elements of statistical learning: data mining, inference, and prediction (2nd edn). Springer: New York, NY, 2009.
40. Green DM, Swets JA,. Signal Detection Theory and Psychophysics. Wiley: New York, 1966.
41. Murphy AH,. A New Vector Partition of the Probability Score. Journal of Applied Meteorology 1973; 12: 595-600. DOI: 10.1175/1520-0450(1973)012<0595:ANVPOT>2.0.CO;2.
42. Mason S, Stephenson D,. How do we know whether seasonal climate forecasts are any good? In Seasonal Climate: Forecasting and Managing Risk. Springer: Dordrecht, 2007; 265-296.
43. Ashcroft LC, Pezza AB, Simmonds I,. Cold events over southern Australia: synoptic climatology and hemispheric structure. Journal of Climate 2009; 22: 6679-6698.
44. Agresti A,. Categorical Data Analysis (2nd edn). Wiley: New York, 2002.
45. Operational implementation of the ACCESS numerical weather prediction systems. NMOC Operations Bulletin 2010; 83: 1-34.