Estimation de l'incertitude des prédictions de production éolienne.

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Pinson, Pierre (2006) Estimation de l'incertitude des prédictions de production éolienne. Doctorat Energétique, ENSMP - CEP Centre Energétique et Procédés, ENSMP.

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Résumé

L’énergie éolienne connaît un développement considérable en Europe. Pourtant, le caractère intermittent de cette énergie renouvelable introduit des difficultés pour la gestion du réseau électrique. De plus, dans le cadre de la dérégulation des marchés de l’électricité, l’énergie éolienne est pénalisée par rapport aux moyens de production contrôlables. La prédiction de la production éolienne à des horizons de 2-3 jours aide l’intégration de cette énergie. Ces prédictions consistent en une seule valeur par horizon, qui correspond à la production la plus probable. Cette information n’est pas suffisante pour définir des stratégies de commerce ou de gestion optimales. C’est pour cela que notre travail se concentre sur l’incertitude des prédictions éoliennes. Les caractéristiques de cette incertitude sont décrites à travers une analyse des performances de certains modèles de l’état de l’art, et en soulignant l’influence de certaines variables sur les moments des distributions d’erreurs de prédiction. Ensuite, nous décrivons une méthode générique pour l’estimation d’intervalles de prédiction. Il s’agit d’une méthode statistique non-paramétrique qui utilise des concepts de logique floue pour intégrer l’expertise acquise concernant les caractéristiques de cette incertitude. En estimant plusieurs intervalles à la fois, on obtient alors des prédictions probabilistes sous forme de densité de probabilité de production éolienne pour chaque horizon. La méthode est évaluée en terme de fiabilité, finesse et résolution. En parallèle, nous explorons la possibilité d’utiliser des prédictions ensemblistes pour fournir des ‘prévisions d’erreur’. Ces prédictions ensemblistes sont obtenues soit en convertissant des prévisions météorologiques ensemblistes (fournies par ECMWF ou NCEP), soit en appliquant une approche de décalage temporel. Nous proposons une définition d’indices de risque, qui reflètent la dispersion des ensembles pour un ou plusieurs horizons consécutifs. Une relation probabiliste entre ces indices de risque et le niveau d’erreur de prédiction est établie. Dans une dernière partie, nous considérons la participation de l’énergie éolienne dans les marchés de l’électricité afin de démontrer la valeur de l’information ‘incertitude’. Nous expliquons comment définir des stratégies de participation à ces bourses de l’électricité avec des prédictions déterministes ou probabilistes. Les bénéfices résultant d’une estimation de l’incertitude des prédictions éoliennes sont clairement démontrés.

Références Bibliographiques

[1] T. Ackermann, R. Leutz, and J. Hobolm. World-wide offshore wind potential and European projects. In Power Engineering Society Summer Meeting, 2001 IEEE, volume 1, pages 4–9, 2001.

[2] M.C. Alexiadis, P. S. Dokopoulos, and H. S. Sahsamanoglou. Wind speed and power forecasting based on spatial correlation models. IEEE Trans. on Energy Conv., 14(3), September 1999.

[3] A. P. Alves da Silva and L. S.Moulin. Confidence intervals for neural network based short-term load forecasting. IEEE Trans. on Power Syst., 15(4), November 2000.

[4] E. Anahua, F. Boettcher, S. Barth, J. Peinke, and M. Lange. Stochastic analysis of the power output for a wind turbine. In CD-Proc. of the 2004 European Wind Energy Conference, EWEC’04, London, United Kingdom, November 2004.

[5] F. Atger. The skill of ensemble prediction systems. Mon.Wea. Rev., 127:1941–1957, September 1999.

[6] B. Bailey,M. C. Brower, and J. C. Zack. Short-term wind forecasting - Development and application

of amesoscale model. In Proc. of the 1999 European Wind Energy Conference, EWEC’99, Nice, France, pages 1062–1065,March 1999.

[7] R. T. Baillie and T. Bollerslev. Prediction in dynamic models with time-dependent conditional variances. J. Econometrics, 51:91–113, 1992.

[8] L. Balea, G. Kariniotakis, N. Siebert, and E. Peirano. Quantification of capacity credit and reserve demand from the large-scale integration of wind energy in the French power system. In CD-Proc. of the 2004 Global Wind power Conference, Chicago, Illinois (USA),March 2004.

[9] T. Barker. The relationship between spread and error in extended range forecasts. J. Climate, 4:733–742, 1991.

[10] R. J. Barthelmie, S. C. Pryor, S. Frandsen, and S. E. Larsen. Analytical modelling of large wind farm clusters. In Proc. of the 2004 EAWE Conference, European Academy for Wind Energy, ‘The science of making torque from wind’, Delft, The Netherlands, April 2004.

[11] J. P. Barton and D. G. Ingfield. Energy storage and its use with intermittent renewable energy. IEEE Trans. on Energy Conv., 19(2):441–447, June 2004.

[12] G. N. Bathurst and G. Strbac. Value of combining energy storage and wind in short-term energy and balancing markets. Electric Power Systems Research, 67:1–8, 2003.

[13] G. N. Bathurst, J. Weatherhill, and G. Strbac. Trading wind generation in short-term energy markets. IEEE Trans. on Power Syst., 17(3):782–789, Aug. 2002.

[14] R. Belhomme. Wind power developments in France. In 2002 IEEE Power engineering society winter meeting, volume 1, pages 357–358, 2002.

[15] K. P. Bennet and C. Campbell. Support Vector Machines: hype or hallelujah? SIGKDD Explorations, 2(2):1–13, December 2000.

[16] H. G. Beyer, D. Heinemann, H. Mellinghoff, K.Monnich, and H.-P. Waldl. Forecast of regional wind power output of wind turbines. In Proc. of the 1999 European Wind Energy Conference, EWEC’99, Nice, France, pages 1070–1073,March 1999.

[17] H. G. Beyer, T. Pahlke, W. Schmidt, H.-P. Waldl, and U. de Witt. Wake effects in a linear wind farm. J. of Wind Eng. & Ind. Aerodyn., 51:303–318, 1994.

[18] H. Bindner and P. Lundsager. Integration of wind power in the power system. In IECON 02, Industrial Electronics Society, IEEE, 28th Annual Conference on the, volume 4, pages 3309–3316, 2002.

[19] A. Boogert and D. Y. Dupont. On the effectiveness of the anti-gaming policy between the day ahead and real-time electricity markets in the Netherlands. Energy Economics, 27(5):752–770, September 2005.

[20] S. Bordignon and F. Lisi. Interval prediction for chaotic time-series. Metron, 59(3-4):117–140, 2001.

[21] G. E. P. Box and G.M. Jenkis. Time-Series Analysis, Forecasting an Control. Holden-Day, 1970.

[22] J. B. Bremnes. Probabilistic wind power forecasts using local quantile regression. Wind Energy, 7(1):47–54, January-March 2004.

[23] H. E. Brooks and C. A. Doswell. A comparison of measures-oriented and distributions oriented approaches to forecast verification. Wea. Forecasting, 11:288–303, 1996.

[24] B. G. Brown, R.W. Watz, and A. H. Murphy. Time-series models to simulate and forecast wind speed and wind power. J. App.Met., 23(8):1184–1195, 1984.

[25] K. J. Brundage, S. G. Benjamin, B. Schwartz, and M. N. Schwartz. Wind energy forecasts using timel agged ensembles. In Proc. of the 21st Conference on Weather Analysis and Forecasting/17th Conference on Numerical Weather Prediction, Amer.Meteor. Soc., August 2005.

[26] K. J. Brundage, S. G. Benjamin, and M. N. Schwartz. Wind energy forecasts and ensemble uncertainty fromthe RUC. In Proc. of the Ninth Conference on Mesoscale Processes, Fort Lauderdale, Florida (USA), Amer. Meteor. Soc., pages 343–345, 2001.

[27] C. Buckley, N. Scott, S. Snodin, and P. Gardner. Review of impacts of high wind penetration in electricity networks. Technical report, Garrad Hassan, 2004. Technical report 2299/PR/01 E.

[28] R. Buizza. Chaos and weather prediction. ECMWF, Meteorological Trainng Course Lecture Series, 2002.

[29] R. Buizza, P. L. Houtekamer, Z. Toth, G. Pellerin, M. Wei, and Y. Zhu. A comparison of the ECMWF, MSC and NCEP global ensemble prediction systems. Mon. Wea. Rev., 133(5):1076–1097, 2005.

[30] R. Buizza, M.Miller, and T. N. Palmer. Stochastic representation of model uncertainties in the ECMWF Ensemble Prediction System. Quat. J. Royal Met. Soc., 125:2887–2908, 1999.

[31] D. W. Bunn. Forecasting loads and prices in competitive power markets. Proc. of the IEEE, 88(2), February 2000.

[32] D.W. Bunn and E. D. Farmer. Comparative Models for Electrical Load Forecasting. John Wiley & Sons, 1985.

[33] L. Butler and K. Neuhoff. Comparison of feed in tariff, quota and auction mechanisms to support wind power development. Cambridge Working Papers in Economics CWPE 0503 - CMI Working Paper 70, Cambridge-MIT Institute, 2004.

[34] D. Cabezon, I. Marti, M. J. San Isidro, and I. Perez. Comparison of methods for power curve modelling. In CD-Proc. of the 2004 Global Wind power Conference, Chicago, Illinois (USA), March 2004.

[35] J. G. Carney, P. Cunningham, and U Bhagwan. Confidence and prediction intervals for neural network ensembles. In Proc. of the International Joint Conference on Neural Networks 1999, 1999. paper 2090.

[36] E. D. Castronuovo and J. A. Pec¸as Lopes. On the optimization of the daily operation of a wind hydro power plant. IEEE Trans. on Power Syst., 19(3):1599–1606, August 2004.

[37] B. Chabot. Bilan du développement de l’énergie éolienne en France en fin juin 2003. ADEME report, ADEME, 2003. In French.

[38] B. Chabot. éolien: état des lieux et perspectives. Systèmes Solaires, 155:66–73,May-June 2003. In French.

[39] W. S. Chan, S. H. Cheung, and K. H. Wu. Multiple forecasts with autoregressive time-series models: case-studies. Mathematics and Computers in Simulation, 64:421–430, 2004.

[40] J. G. Charney, R. Fjørtoft, and J. von Neumann. Numerical integration of the barotropic vorticity equation. Tellus, 2:237–254, 1950.

[41] C. Chatfied. Time-Series Forecasting. Chapman & Hall/CRC, 2000.

[42] C. Chatfied. Confessions of a pragmatic statistician. The Statistician, 51:1–20, 2002.

[43] C. Chevallier. Développement et évaluation de stratégies de prédiction éolienne en vue de la participation à un marché de l’électricité dérégulé. Master’s thesis, Ecole des Mines de Paris, Paris, France, 2004. in French.

[44] P. F. Christoffersen. Evaluating interval forecasts. Intern. Econom. Rev., 39(4):841–862, 1998.

[45] R. T. Clemen and R. L.Winkler. Combining probability distributions from experts in risk analysis. Risk Analysis, 19(2):187–203, 1999.

[46] M. P. Clements. Editorial - Some possible directions for future research. Int. J. Forecasting, 19(1):1–3, 2003.

[47] M. P. Clements. Evaluating Econometric Forecasts of Economic and Financial Values. Palgrave Macmillan, 2005.

[48] M. P. Clements and D. F. Hendry. A Companion to Economic Forecasting. Blackwells, 1998.

[49] M. P. Clements and N. Taylor. Bootstrapping prediction intervals for autoregressive models. Int. J. Forecasting, 17(2):247–267, 2001.

[50] A. J. Conejo, J. Contreras, R. Espmola, and M. A. Plazas. Forecasting electricity prices for a day-ahead pool-based electricity energy market. Int. J. Forecasting, 21(3):435–462, 2005.

[51] A. Crespo, J. Hernandez, and S. Frandsen. Survey of modelling methods for wind turbine wakes and wind farms. Wind Energy, 2(1):1–24, 1999.

[52] N. Cristianini and J. Shawe-Taylor. In Introduction to Support Vector Machines and other Kernel-based Learning Methods. Cambridge University Press, 2000.

[53] I. G. Damousis,M. C. Alexiadis, J. B. Theocharis, and P. S. Dokopoulos. A fuzzy model for wind speed prediction and power generation in wind parks using spatial correlation. IEEE Trans. on Energy Conv., 19(2):352–361, June 2004.

[54] R. De Veaux, J. Schumi, J. Schweinsberg, D. Shellington, and L. H. Ungar. Prediction intervals for neural networks via nonlinear regression. J. Amer. Stat. Ass., 40(4):273–282,November 1998.

[55] E. DeMeo. Implications of wind energy scheduling requirements for wind energy. NWCC Transmission Issue Briefs, National Wind Coordinating Committee (NWCC),US, available online : www.nationalwind.org, August 2003.

[56] R. Doherty, E. Denny, and M. O’Malley. System operation with a significant wind power penetration. In Proc. of the IEEE Power Eng. Soc. General Meeting, Denver, Colorado (USA), volume

1, pages 1002–1007, June 2004.

[57] R. Doherty and M. O’Malley. A new approach to quantify reserve demand in systems with significant installed wind capacity. IEEE Trans. on Power Syst., 20(2):587–595,May 2005.

[58] S. D. Dubey. Normal and weibull distributions. Naval Res. Logistics Quart., 14:69–79, 1967.

[59] R. Dybowski and S. J. Roberts. Confidence intervals and prediction intervals for feed-forward neural networks. In R. Dybowski and V. Gant, editors, Clinical Applications of Artificial Neural Networks. Cambridge University Press, 2000.

[60] B. Efron. Bootstrap methods: another look at the jackknike. Ann. Statist., 7:1–26, 1979.

[61] B. Efron and R. J. Tibshirani. An Introduction to the Bootstrap. Chapman & Hall/CRC, 1993.

[62] S. Engstrom, H. Ganander, and R. Lindstrom. Short-term variations in the output of wind turbines. DEWI Magazine, 19:27–30, 2001.

[63] S. Enomoto and co authors. Prediction of power output from wind farmusing local meteorological analysis. In Proc. of the 2001 European Wind Energy Conference, EWEC’01, Copenhagen, Denmark, pages 822–825, June 2001.

[64] C. Ensslin, B. Ernst, K. Rohrig, and F. Schlogl. On-line monitoring and prediction of wind power in German transmission system operation center. In CD-Proc. of the 2003 European Wind Energy Conference, EWEC’03,Madrid, Spain, June 2003.

[65] B. Ernst, K. Rohrig, P. Schorn, and H. Regber. Managing 3000 MW power in a transmission system operation center. In Proc. of the 2001 European Wind Energy Conference, EWEC’01, Copenhagen, Denmark, pages 890–893, June 2001.

[66] European Commission. Directive 2001/77/EC of the European Parliament and of the Council, on the promotion of electricity produced from renewable energy sources in the internal

electricity market. Official Journal of the European Commission, September 2001.

[67] A. Fabbri, T. G. Gomez San Roman, J. R. Rivier Abbad, and V. H. Méndez Quezada. Assessment of the cost associated with wind generation prediction errors in a liberalized electricity market. IEEE Trans. on Power Syst., 20(3):1440–1446, 2005.

[68] U. Focken,M. Lange, K.Monnich, H.-P.Waldl, H. G. Beyer, and A. Luig. 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. J. of Wind Eng. & Ind. Aerodyn., 90:231–246, 2002.

[69] U. Focken, M. Lange, and H.-P. Waldl. Previento - A wind power prediction system with an innovative upscaling algorithm. In Proc. of the 2001 European Wind Energy Conference, EWEC’01, Copenhagen, Denmark, pages 826–829, June 2001.

[70] U. Focken, M. Lange, and H.-P. Waldl. Previento - Regional wind power prediction with risk control. In CD-Proc. of the 2002 Global Wind power Conference, Paris, France, April 2002.

[71] H. Fukuda. The development of a wind velocity prediction method based on a data-mining type auto-regressive model. In Proc. of the 2001 European Wind Energy Conference, EWEC’01, Copenhagen, Denmark, pages 741–744, June 2001.

[72] C. Garcia Barquero and P. de Arriba Segurado. Case study # 12: Spanish Royal Decree 2818/1998 - ‘Special regime of the electricity market’. REACT - Renewable Energy Action, Altener AL/2002/157, October 2004.

[73] N. Gasset, G. J. Poitras, Y. Gagnon, and C. Brothers. Atmospheric boundary layer flow over a coastal cliff. In CD-Proc. of the 2003 European Wind Energy Conference, EWEC’03, Madrid, Spain, June 2003.

[74] C. Genest and K. J.McConway. Allocating the weights in the linear opinion pool. J. Forecasting, 9:53–73, 1990.

[75] G. Giebel. On the benefits of distributed generation of wind energy in Europe. PhD thesis, University Carl von Ossietzky, Oldenburg, Germany, 2000.

[76] G. Giebel, R. Brownsword, and 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.

[77] G. Giebel, L. Landberg, J. Badger, K. Sattler, H. Feddersen, T. S. Nielsen, H. Aa. Nielsen, and H. Madsen. Using ensemble forecasting for wind power. In CD-Proc. of the 2003 European Wind Energy Conference, EWEC’03,Madrid, Spain, June 2003.

[78] G. Giebel, L. Landberg, G. Kariniotakis, and R. Brownsword. State-of-the-art on methods and software tools for short-term prediction of wind energy production. In CD-Proc. of the 2003 European Wind Energy Conference, EWEC’03,Madrid, Spain, June 2003.

[79] G. Giebel, L. Landberg, and T. S. Nielsen. The ZEPHYR-project: The next generation prediction system. In Proc. of the 2001 European Wind Energy Conference, EWEC’01, Copenhagen, Denmark, pages 777–780, June 2001.

[80] T. Gneiting, F. Balabdaoui, and A. E. Raftery. Probabilistic forecasts, calibration and sharpness. Technical report, University of Washington, Department of Statistics, May 2005. Technical report no. 483.

[81] T. Gneiting and A. E. Raftery. Strictly proper scoring rules, prediction, and estimation. Technical report, University of Washington, Department of Statistics, 2004. Technical report no. 463.

[82] T. Gneiting and A. E. Raftery. Weather forecasting with ensemble methods. Science, 310(5746):248–249, 2005.

[83] M. Grigoletto. Bootstrapping prediction intervals for autoregressions: some alternatives. Int. J. Forecasting, 14(4):447–456, 1998.

[84] E. P. Grimit. Redefining the ensemble spread-skill relationship from a probabilistic perspective. NCEP invited presentation, Camp Spring, Maryland (USA), April 2004.

[85] E. P. Grimit and C. F. Mass. Initial results of a mesoscale short-range ensemble forecasting systemover the Pacific Northwest. Wea. Forecasting, 132:192–204, 2002.

[86] E. P. Grimit and C. F. Mass. Measuring the ensemble spread-skill reliationship from a probabilistic perspective: stochastic ensemble results. submitted to Monthly Weather Review, June 2005.

[87] G. J. Hahn and W. Q. Meeker. Statistical Intervals - A Guide for Practitioners. John Wiley & Sons, 1991.

[88] P. Hall and A. Rieck. Improving coverage accuracy of nonparametric prediction intervals. J. Royal Stat. Soc., 63(4):717–725, 2001.

[89] T. M. Hamill. Interpretation of rank histograms for verifying ensemble forecasts. Mon. Wea. Rev., 129, 2000. Notes and Correspondence.

[90] T. M. Hamill. Evaluating forecaster’s rules of thumb: a study of d(prog)/dt. Wea. Forecasting, 18:933–937, 2003. Notes and Correspondence.

[91] N.Hatziargyriou and A. Zervos. Wind power developments in Europe. Proc. of the IEEE Special Issue, 89(12):1765–1782, 2001.

[92] T. Heskes. Practical confidence and prediction intervals. In M. Mozer, M. Jordan, and T. Petsche, editors, Advances in Neural Information Processing Systems 9. MIT Press, Cambridge, 1997.

[93] M. Hibon and T. Evgeniou. To combine or not to combine: selecting among forecasts and their combinations. Int. J. Forecasting, 21(3):15–24, 2005.

[94] B. F. Hobbs, S. Jitprapaikulsarn, S. Konda, V. Chankong, K. A. Loparo, and D. J. Maratukulam. Analysis of the value for unit commitment of improved load forecasts. IEEE Trans. on Power Syst., 14(4), November 1999.

[95] R. N. Hoffman and E. Kalnay. Lagged average forecasting, an alternative to monte-carlo forecasting. Tellus, 35A:100–118, 1983.

[96] J. R. Holton. An Introduction to Dynamic Meteorology. International Geophysics Series. Academic Press, 1992.

[97] H. Holttinen. The impact of large scale wind power production on the Nordic power system. PhD thesis, Helsinki University of Technology, Espoo, Finland, 2004.

[98] H. Holttinen. Optimal electricity market for wind power. Energy Policy, 33(16):2052–2063, November 2004.

[99] H. Holttinen and R. Hirvonen. Power system requirements for wind power. In T. Ackermann, editor,Wind Power in Power Systems. John Wiley & Sons, 2005.

[100] P. L. Houtekamer. Global and local skill forecasts. Mon.Wea. Rev., 121:1834–1846, 1993.

[101] J. J. G. Hwang and A. A. Ding. Prediction intervals for artificial neural networks. J. Amer. Stat. Ass., 92:748–757, 1997.

[102] R. J. Hyndman. Highest-density forecast regions for non-linear and non-normal time-series models. J. Forecasting, 14:431–441, 1995.

[103] IEA. Variability of wind power and other renewables. Technical report, IEA (International Energy Agency), 2005. Paris: IEA/OCDE.

[104] J. U. Jørgensen and C.Mohrlen. Verification of ensemble prediction systems for a new market: wind energy. Technical report, Sustainable Energy Research Group, University College Cork, Ireland, June 2003. ECMWF special project interim report 2.

[105] J. U. Jørgensen, C. Mohrlen, B. O’Gallachoir, K. Sattler, and E. J. McKeogh. HIRPOM: description of an operational numerical wind power prediction model for large-scale integration of on- and offshore wind power in Denmark. In CD-Proc. of the 2002 Global Wind power Conference, Paris, France, April 2002.

[106] M. Jørgensen and D. I. K. Sjøberg. An effort prediction interval approach based on the empirical distribution of previous estimation accuracy. Information & Software Technology, 45:123–136, 2003.

[107] C. G. Justus, W. R. Hargraves, A. Mikhail, and D. Graber. Methods for estimating wind speed frequency distribution. J. App.Met., 17(3):350–353, 1978.

[108] R. E. Kalman. A new approach to linear filtering and prediction problems. Trans. ASME, J. Basic Eng., 82(Ser. D):35–45, 1960.

[109] E. Kalnay, S. J. Lord, and R. D.McPherson. Maturity of operational numerical weather prediction: medium range. Bull. Am. Met. Soc., 79(12):2753–2769, 1998.

[110] G. Kariniotakis. Contribution to the development of an advanced control system for autonomous wind-diesel power systems. PhD thesis, Ecole des Mines de Paris, France, 1996.

[111] G. Kariniotakis and co authors. What performance can be expected by short-term wind power prediction models depending on site characteristics? In CD-Proc. of the 2004 European Wind Energy Conference, EWEC’04, London, United Kingdom, November 2004.

[112] G. Kariniotakis and D. Mayer. An advanced on-line wind resource prediction system for the optimal management of wind parks. In CD-Proceedings of the MedPower Conference, Athens, Greece, November 2002.

[113] G. Kariniotakis, E. Nogaret, and A. G. Dutton. Wind power forecasting using advanced neural networks models. IEEE Trans. on Energy Conv., 11(4):762–767, December 1996.

[114] G. Kariniotakis, E. Nogaret, A. G. Dutton, J. A. Halliday, and A. Androutsos. Evaluation of advanced wind power and load forecasting methods for the optimal management of isolated power systems. In Proc. of the 1999 European Wind Energy Conference, EWEC’99, Nice, France, pages 1082–1085,March 1999.

[115] G. Kariniotakis and P. Pinson. Evaluation of the MORE-CARE wind power prediction platform. Performance of the fuzzy logic based models. In CD-Proc. of the 2003 European Wind Energy Conference, EWEC’03,Madrid, Spain, June 2003.

[116] G. Kariniotakis and P. Pinson. Uncertainty of short-term wind power forecasts - A methodology for on-line assessment. In Proc. of the 2004 PMAPS Conference, ‘Probabilistic Methods Applied to Power Systems’, IEEE PMAPS 2004, Ames, Iowa (USA), pages 729–736, September 2004.

[117] G. Kariniotakis, P. Pinson, and A. G. Dutton. MORE-CARE evaluation report - Evaluation results for the case study of Ireland. MORE-CARE European Project, Deliverable report D13, May 2003.

[118] N. K. Kasabov and Q. Song. DENFIS: Dynamic Evolving Neural-Fuzzy Inference System and its application for time-series prediction. IEEE Trans. on Fuzzy Systems, 10(2), April 2002.

[119] I. Katic, J. Højstrup, and N. O. Jensen. A simple model for cluster efficiency. In W. Palz and E. Sesto, editors, Proc. of the European Wind Energy Conference and Exhibition, volume 1, pages 407–410. A. Raguzzi, 1986.

[120] A. B. Koehler. An inappropriate prediction interval. Int. J. Forecasting, 6(4):557–558, 1990.

[121] M. Korpaas, A. T. Holen, and R. Hildrum. Operation and sizing of energy storage for wind power plants in amarket system. Electrical Power and Energy Systems, 25:599–606, 2003.

[122] B. B. Kristoffersen, B. Donslun, and P. Børre Eriksen. Impacts of large-scale wind power on the power market. In Proc. of the Fourth International Workshop on Large-Scale Integration of Wind Power and Transmission Networks for Offshore Wind Farms, Billund, Denmark, Oct. 2003.

[123] J.-P. Lam and M. R. Veall. Bootstrap prediction intervals for single period regression forecasts. Int. J. Forecasting, 18(1):125–130, 2002.

[124] L. Landberg. A mathematical look at a physical power prediction model. Wind Energy, 1:23–28, 1998.

[125] L. Landberg. Short-term prediction of the power production from wind farms. J. of Wind Eng. & Ind. Aerodyn., 80:207–220, 1999.

[126] L. Landberg, G. Giebel, L. Myllerup, J. Badger, H. Madsen, and T. S. Nielsen. Poor-man’s ensemble forecasting for error estimation. In Proc. of the 2002 American Wind Energy Association conference, Portland, Oregon, June 2002.

[127] L. Landberg and S. J.Watson. Short-term prediction of local wind conditions. Boundary-Layer Meteorology, 70:171–195, 1994.

[128] B. Lange, S. E. Larsen, J. Højstrup, and R. J. Barthelmie. Importance of thermal effects and sea surface roughness for offshore wind ressource assessment. J. of Wind Eng. & Ind. Aerodyn., 92:959–988, 2004.

[129] M. Lange. Analysis of the uncertainty of wind power predictions. PhD thesis, University Carl von Ossietzky, Oldenburg, Germany, 2003.

[130] M. Lange. On the uncertainty of wind power predictions - Analysis of the forecast accuracy and statistical distribution of errors. Trans. of the ASME, J. Solar Energy Eng., 127(2):177–194, May 2005.

[131] M. Lange and D. Heinemann. Relating the uncertainty of short-term wind speed prediction to meterological situations with methods from synoptic climatology. In CD-Proc. of the 2003 European Wind Energy Conference, EWEC’03,Madrid, Spain, June 2003.

[132] M. Lange, J. Tambke, U. Focken, J.-O.Wolff, and J. A. T. Bye. Forecasting offshore wind power. In Proc. of the 2004 EAWE Conference, European Academy for Wind Energy, ‘The science of making

torque from wind’, Delft, The Netherlands, April 2004.

[133] M. Lange and H.-P. Waldl. Accuracy of short-term wind power prediction depending on meteorological conditions. In CD-Proc. of the 2002 Global Wind power Conference, Paris, France, April 2002.

[134] K. Larson and T. Gneiting. Advanced short-range wind energy forecasting technologies - Challenges, solutions and validation. In CD-Proc. of the 2004 Global Wind power Conference, Chicago, Illinois (USA),March 2004.

[135] K. Larson and K. Westrick. Short-term wind power forecasting using off-site observations. Wind Energy, 9((1-2)):55–62, 2005.

[136] C. E. Leith. Theoretical skill of Monte Carlo forecasts. Mon.Wea. Rev., 102:409–418, 1974.

[137] S. Li, D. C. Wunsch, and E. A. O’Hair. Using neural networks to estimate wind power turbine generation. IEEE Trans. on Energy Conv., 16(3):276–282, September 2001.

[138] U. Linnet. Tools supporting wind energy trade in deregulated markets. Master’s thesis, Informatics and Mathematical Modeling, Technical University of Denmark, Kgs. Lyngby, Denmark, 2005. IMM-Thesis-2005-56.

[139] E. N. Lorenz. A study of the predictability of a 28-variable atmospheric model. Tellus, 17:321–333, 1965.

[140] E. N. Lorenz. The predictability of a flow which possesses many scales of motion. Tellus, 21:289–307, 1968.

[141] P. Louka, G. Galanis, N. Siebert, G. Kariniotakis, P. Katsafados, G. Kallos, and I. Pytharoulis. Improvements in wind speed forecasts for wind power prediction purposes using Kalman filtering. In Proc. of the 5th Conference on Mathematical Methods in Science & Engineering, 2005 CMMSE, June 2005.

[142] A. Luig, S. Bofinger, and H. G. Beyer. Analysis of confidence intervals for the prediction of regional wind power output. In Proc. of the 2001 European Wind Energy Conference, EWEC’01, Copenhagen, Denmark, pages 725–728, June 2001.

[143] H. Lund and E. Munster. Management of surplus electricity-production from a fluctuating renewable-energy source. Applied Energy, 76:65–74, 2003.

[144] R. Madlener and M. Kaufmann. Power exchange spot market trading in europe: theoretical considerations and empirical evidence. OSCOGEN (Optimisation of Cogeneration Systems in

a Competitive Market Environment)- Project Deliverable 5.1b,March 2002.

[145] H. Madsen, P. Pinson, H. Aa. Nielsen, T. S. Nielsen, and G. Kariniotakis. Standardizing the performance evaluation of short-term wind power prediction models. Wind Engineering, 29(6):475–489, 2005.

[146] M. Magnusson and L. Wern. Wind energy predictions using CFD and HIRLAM forecast. In Proc. of the 2001 European Wind Energy Conference, EWEC’01, Copenhagen, Denmark, pages 861–863, June 2001.

[147] S. Makridakis, M. Hibon, E. Lusk, and M. Belhadjali. Confidence intervals - An empirical investigation of the series in the M-competitions. Int. J. Forecasting, 3(3-4):489–508, 1987.

[148] S. Makridakis, S. C. Wheelwright, and V. E. McGee. Forecasting: Methods and Applications. John Wiley & Sons, 2nd edition, 1983.

[149] I. Marti and H. Madsen. Prediction models in complex terrain. In Proc. of the 2001 European Wind Energy Conference, EWEC’01, Copenhagen, Denmark, pages 875–878, June 2001.

[150] J. Mason. Definition of technical terms in forecast verification and examples of forecast verification scores. In Proc. of the IRI Workshop on Forecast Quality, New York, October 2000.

[151] J. McCann. Operating reserve requirements as wind power penetration increases in the Irish electricity system. SEI report 04-RERDD-011-R-01, Sustainable Energy Ireland (SEI), 2004. available online: www.irish-energy.ie.

[152] E. McCarthy. Wind speed forecasting in the central California wind resource area. In EPRIDOE-NREL WInd Energy Forecasting Meeting, Burlingame, California (USA),March 1998.

[153] S.McNees. Forecast uncertainty: can it be measured ? mimeo, Federal Reserve Bank of Boston, Boston,Massachusetts (USA), 1995.

[154] K. Methaprayoon, W. J. Lee, C. Yingvivatanapong, and J. Liao. An integration of ANN wind power estimation into UC considering forecast uncertainty. In Proc. of the Industrial and Commercial

Power Systems Technical Conference, 2005 IEEE, pages 116–124,May 2005.

[155] D.Milborrow. Penalties for intermittent energy sources. Working paper for the PIU Energy Review - available online: www.cabinet-office.gov.uk/innovation/2002/energy/workingpapers, December 2001.

[156] V. Miranda and L. M.. Proenca. Probabilistic choice vs. risk analysis - Conflicts and synthesis in power system planning. IEEE Trans. on Power Syst., 13(3):1038–1043, 1998.

[157] C. Mohrlen. On the accuracy of land cover data in NWP forecasts for high resolution wind energy prediction. In Proc. of the 2001 European Wind Energy Conference, EWEC’01, Copenhagen, Denmark, pages 854–857, June 2001.

[158] C. Mohrlen. Uncertainty in wind energy forecasting. PhD thesis, University College Cork, National University of Ireland, Cork, Ireland, 2004.

[159] C.Mohrlen and J. U. Jørgensen. Verification of ensemble prediction systems for a new market: wind energy. Technical report, Sustainable Energy Research Group, University College Cork, Ireland, June 2005. ECMWF special project interim report 3.

[160] C.Morlen, J. U. Jørgensen, and E. J.McKeogh. Power predictions in complex terrain with an operational numerical weather prediction model in Ireland including ensemble forecasting. In Proc. of the 2002WorldWind Energy Conference, Berlin, Germany, June 2002.

[161] F.Molteni, R. Buizza, T.N. Palmer, and T. Petroliagis. The ECMWF ensemble prediction system: methodology and validation. Quat. J. Royal Met. Soc., 122:73–119, 1996.

[162] D.Moon, D. Christenson, and R. Chevallaz-Perrier. Support Vector Machines technology coupled with physics-based modelling for wind facility power production forecasting. In CD-Proc. of the 2004 Global Wind power Conference, Chicago, Illinois (USA),March 2004.

[163] A. Moore and R. Kleeman. Skill assessment for ENSO using ensemble prediction. Quat. J. Royal Met. Soc., 124:557–584, 1998.

[164] P. E. Morthorst. Wind power and the conditions at a liberalized power market. Wind Energy, 6:297–308, July-September 2003.

[165] A. H. Murphy. What is a good forecast? An essay on the nature of goodness in weather forecasting. Wea. Forecasting, 8(2):281–293, 1993.

[166] A. H. Murphy and E. S. Epstein. Skill scores and correlation coefficients in model verification. Mon.Wea. Rev., 117:784–794, 1989.

[167] A. H.Murphy and R. L.Winkler. A general framework for forecast verification. Mon.Wea. Rev., 115:1330–1338, 1987.

[168] J. M. Murphy. The impact of ensemble forecasts on predictability. Quat. J. Royal Met. Soc., 114:463–493, 1988.

[169] C. S. Nielsen and H. F. Ravn. Criteria in short-term wind power prognosis. In CD-Proc. of the 2003 European Wind Energy Conference, EWEC’03, Madrid, Spain, June 2003.

[170] H. Aa. Nielsen. Analysis and simulation of prediction errors for wind power productions reported to nord pool. IMM-Eltra project report, Informatics and Mathematical Modeling, Technical University of Denmark, Denmark, 2002. in Danish.

[171] H. Aa. Nielsen, T. S. Nielsen, and H. Madsen. Using quantile regression to extend an existing wind power forecasting system with probabilistic forecasts. In Proc. of the 2004 European

Wind Energy Conference, EWEC’04, Scientific Track, London, United Kingdom, pages 34–38, November 2004.

[172] H. Aa. Nielsen, T. S. Nielsen, H. Madsen, and K. Sattler. Wind power ensemble forecasting. In CD-Proc. of the 2004 Global Wind power Conference, Chicago, Illinois (USA),March 2004.

[173] H. Aa. Nielsen, D. Yates, H. Madsen, T. S. Nielsen, J. Badger, G. Giebel, L. Landberg, K. Sattler, and H. Feddersen. Analysis of the results of an on-line wind power quantile forecasting system. Technical report, Informatics and Mathematical Modeling, Danish Technical University, October 2005. PSO (FU 2101) project report.

[174] L. H. Nielsen and co authors. Wind power and a liberalised North European electricity exchange. In Proc. of the 1999 European Wind Energy Conference, EWEC’99, Nice, France, pages 379–382,March 1999.

[175] T. S. Nielsen, A. Joensen, H. Madsen, L. Landberg, and G. Giebel. A new reference model for wind power forecasting. Wind Energy, 1:29–34, 1998.

[176] T. S. Nielsen and H.Madsen. ZEPHYR - The prediction models. In Proc. of the 2001 European Wind Energy Conference, EWEC’01, Copenhagen, Denmark, pages 868–871, June 2001.

[177] T. S.Nielsen and H.Madsen. Prediction of regional wind power. In CD-Proc. of the 2002 Global Wind power Conference, Paris, France, April 2002.

[178] T. S. Nielsen, H. Aa. Nielsen, and H. Madsen. Prediction of wind power using time-varying coefficient-functions. In Proc. of the 15th IFAC World Congress, Barcelona, Spain, July 2002. Elsevier, ISBN: 0-08-044295-1.

[179] J.-M. Noel and R. Chevallaz-Perrier. Wind resource assessment with Aria Wind in a complex site. In Proc. of the 2001 European Wind Energy Conference, EWEC’01, Copenhagen, Denmark, pages 872–874, June 2001.

[180] M. Olsson and L. Soder. Generation of regulating power pice scenarios. In Proc. of the 2004 PMAPS Conference, ‘Probabilistic Methods Applied to Power Systems’, IEEE PMAPS 2004, Ames, Iowa (USA), pages 26–31, September 2004.

[181] D. Orrell, L. A. Smith, J. Barkmeijer, and T. N. Palmer. Model error and operational weather forecasts. Nonlin. Proc. Geophys., 8(6):357–371, November 2001.

[182] T. N. Palmer. Predicting uncertainty in forecasts of weather and climate. Rep. Prog. Phys., 63(2):71–116, 2000.

[183] P. Pinson and G. Kariniotakis. Wind power forecasting using fuzzy neural networks enhanced with on-line prediction risk assessment. In CD-Proc. of the 2003 Bologna Power Tech Conference, IEEE Power Tech 2003, Bologna, Italy, June 2003.

[184] P. Pinson and G. Kariniotakis. On-line adaptation of confidence intervals based on weather stability for wind power forecasting. In CD-Proc. of the 2004 Global Wind power Conference, Chicago, Illinois (USA),March 2004.

[185] P. Pinson, G. Kariniotakis, and D.Mayer. Uncertainty and prediction risk assessment of short term wind power forecasts. In Proc. of the 2004 EAWE Conference, European Academy for Wind Energy, ‘The science of making torque from wind’, Delft, The Netherlands, April 2004.

[186] P. Pinson, H. Aa. Nielsen, T. S. Nielsen, H. Madsen, and G. Kariniotakis. Properties of quantile and interval forecasts of wind generation and their evaluation. In Proc. of the 2006 European Wind Energy Conference, EWEC’06, Scientific Track, Athens, Greece, February 2006.

[187] P. Pinson, T. Ranchin, and G. Kariniotakis. Short-term wind power prediction for offshore wind farms - Evaluation of fuzzy-neural network based models. In CD-Proc. of the 2004 Global Wind power Conference, Chicago, Illinois (USA),March 2004.

[188] P. Pinson, N. Siebert, and G. Kariniotakis. Forecasting of regional wind generation by a dynamic fuzzy-neural networks based upscaling approach. In CD-Proc. of the 2003 European Wind Energy Conference, EWEC’03,Madrid, Spain, June 2003.

[189] S. C. Pryor and R. J. Barthelmie. Persistence of offshore winds: implications for power quality. In Proc. of the 2001 European Wind Energy Conference, EWEC’01, Copenhagen, Denmark, pages 717–720, June 2001.

[190] A. Rauh and J. Peinke. A phenomenological model for the dynamic response of wind turbines to turbulent wind. J. of Wind Eng. & Ind. Aerodyn., 92:159–183, 2004.

[191] N. Ravishankar, L. Shiao-Yen Wu, and J. Glaz. Multiple prediction intervals for time-series: comparison of simultaneous and marginal intervals. J. Forecasting, 10:445–463, 1991.

[192] J. J. Reeves. Bootstrap prediction intervals for ARCH models. Int. J. Forecasting, 20(2):237–248, 2004.

[193] P. J. Roebber. Variability in successive operational model forecasts of maritime cyclogenesis. Wea. Forecasting, 5:586–595, 1990.

[194] J.-S. Roger Jang. ANFIS: Adaptive-Network-based Fuzzy Inference System. IEEE Trans. on systems, man, and cybernetics, 23(3),May-June 1993.

[195] C.-G. Rossby and co authors. Relation between variations in the intensity of the zonal circulation of the atmosphere and the displacements of the semi-permanent centers of action. J. Mar. Res, 2:38–55, 1939.

[196] M. S. Roulston, D. T. Kaplan, J. Hardenberg, and L. A. Smith. Using medium-range weather forecasts to improve the value of wind energy production. Renewable Energy, 28:585–602, 2003.

[197] M. S. Roulston and L. A. Smith. Evaluating probabilistic forecasts using information theory. Mon. Wea. Rev., 130:1653–1660, June 2002. Notes and Correspondence.

[198] Y.-M. Saint Drenant. Wind power predictions analysis - Part 1: Tenne T imbalance price system. Development of a model for Tenne T imbalance price system. ECN report - ECN-I-02-010, Energy research Center of the Netherlands, 2002.

[199] I. Sanchez. Short-term prediction of wind energy production. Int. J. Forecasting, 22(1):43–56, 2006.

[200] D.W. Scott. Multivariate Density Estimation: Theory, Practice and Visualization. John Wiley& Sons, New York, 1992.

[201] M. Sewalt and C. de Jong. Negative prices in electricity markets. Commodities Now - Online journal: www.commodities-now.com, June 2003.

[202] A. Sfetsos. Time-series forecasing of wind speed and solar radiation for renewable energy sources. PhD thesis, Imperial College, London, UK, 1999.

[203] A. Sfetsos. A comparison of various forecasting techniques applied to mean hourly wind speed time series. Renewable Energy, 21:23–35, 2000.

[204] K. Skytte. The regulating power market on the Nordic power exchange Nord Pool: an econometric analysis. Energy Economics, 21:295–308, 1999.

[205] T. A. B. Snijders. On cross validation for predictor evaluation in time-series. In T. K. Dijstra, editor, On Model Uncertainty and its Statistical Implications. Lecture Notes in Economics and Mathematical Systems 307. Oxford University Press, Oxford, 1988.

[206] L. Soder. Reserve margin planning in a wind-hydro-thermal power system. IEEE Trans. on Power Syst., 8(2),May 1993.

[207] T. R. Stewart. Uncertainty, judgement, and error in prediction. In D. Sarewitz, R. A. Pielke, and R. Byerly, editors, Prediction: Science, Decision Making, and the Future of Nature, pages 41–57. Island Press, 2000.

[208] M. Stone. The opinion pool. Annals of Mathematical statistics, 32:1339–1342, 1961.

[209] M. Stone. Cross validation and assessment of statistical predictions (with discussion). J. Royal Stat. Soc., B, 36:111–147, 1974.

[210] J. Sveca and L. Söder. Wind power integration in power systems with bottleneck problems. In CD-Proc. of the 2003 Bologna Power Tech Conference, IEEE Power Tech 2003, Bologna, Italy, June 2003.

[211] N. R. Swanson and H. White. Forecasting economic time-series using flexible versus fixed specification and linear versus nonlinear econometric models. Int. J. Forecasting, 13(4):439–461, 1997.

[212] W. Sweet. Reap the wild wind. IEEE Spectrum, 39:34–39, October 2002.

[213] L. Takacs. A two-step scheme for the advection equation with minimized dissipation and dispersion errors. Mon.Wea. Rev., 113:1050–1065, 1985.

[214] J. Tambke, M. Lange, and U. Focken. Forecasting offshore wind speeds above the North Sea. Wind Energy, 8(1):3–16, January-March 2005.

[215] J. W. Taylor and R. Buizza. Neural network load forecasting with weather ensemble predictions. IEEE Trans. on Power Syst., 17(3):626–632, August 2002.

[216] J. W. Taylor and R. Buizza. Using weather ensemble predictions in electricity demand forecasting. Int. J. Forecasting, 19(1):57–70, 2003.

[217] J.W. Taylor and D.W. Bunn. Investigating improvements in the accuracy of prediction intervals for combination of forecasts: a simulation study. Int. J. Forecasting, 15(3):325–339, 1999.

[218] K. E. Taylor. Summarizing several aspects of model performance in a single diagram. J. Geophys. Res., 106:7183–7192, 2001.

[219] H. Tennekes. Karl Popper and the accountability of numerical forecasting. In New Developments in Predictability, 1991. ECMWF Workshop Proceedings, ECMWF, Reading, United Kingdom.

[220] S.-E. Thor and P.Weis-Taylor. Long-term research and development needs for wind energy for the time frame 2000-2020. Wind Energy, 5:73–75, April-June 2003.

[221] H. Tong. A personal overview of non-linear time-series analysis from a chaos point of view. Scand. J. Stats, 22:399–445, 1995.

[222] Z. Toth and E. Kalnay. Ensemble forecasting at NCEP and the breeding method. Mon. Wea. Rev., 125:3297–3319, 1997.

[223] Z. Toth, O. Tallagrand, G. Candille, and Y. Zhu. Probability and ensemble forecasts. In I.T. Jolliffe and D.B. Stephenson, editors, Forecast Verification: A Practitioner’s Guide in Atmospheric Science. John Wiley & Sons, Ltd, 2003.

[224] Z. Toth, Y. Zhu, and T. Marchock. The use of ensembles to identify forecasts with small and large uncertainty. Wea. Forecasting, 16(4):463–477, 2001.

[225] I. Troen and E. L. Petersen. European Wind Atlas. Risø National Laboratory, 1989.

[226] J. Usaola and P. de Arriba Segurado. Wind prediction in electricity markets. ANEMOS Deliverable Report D8.2, available soon, November 2005.

[227] J. Usaola, O. Ravelo, G. Gonzalez, F. Soto, C. Davila, and B. Diaz-Guerra. Benefits for wind energy in electricity markets from using short term wind power prediction tools - A simulation study. Wind Engineering, 28(1):119–128, 2004.

[228] V. Vapnik. The Nature of Statistical Learning Theory. Springer, 1998.

[229] J. W. Verkaik. A method for the geographical interpolation of wind speed over heterogenous terrain. KNMI HYDRA project report, Royal Meteorological Institute of the Netherlands (KNMI), 2001. available online: www.knmi.nl/samenw/hydra/documents/geograph/.

[230] L. J. Vermeer, J. N. Sørensen, and A. Crespo. Wind turbine wake aerodynamics. Progress in Aerospace Sciences, 39:467–510, 2003.

[231] K. F. Wallis. Forecast uncertainty, its representation and evaluation. Tutorial lecture, IMS Singapore, May 2004.

[232] L.-W.Wang. Adaptive fuzzy systems and control. Prentice Hall, 1994.

[233] R. Watson and L. Landberg. Evaluation of the Prediktor wind power forecasting system in Ireland. In CD-Proc. of the 2003 European Wind Energy Conference, EWEC’03, Madrid, Spain, June 2003.

[234] J. S. Whitaker and A. F. Loughe. The relationship between ensemble spread and ensemble mean skill. Mon. Wea. Rev., 126:3292–3302, 1998.

[235] W. H. Williams and M. L. Goodman. A simple method for the construction of empirical confidence limits for economic forecasts. J. Amer. Stat. Ass., 66(336):752–754, 1971. Applications Section.

[236] Wind Focus. A summary of opinion surveys on wind power. Wind Directions, pages 16–33, September-October 2003. available at www.ewea.org/documents/WD22vi public.pdf.

[237] R. L.Winkler. A decision-theoretic approach to interval estimation. J. Amer. Stat. Ass., 67:187–191, 1972.

[238] R. L.Wobus and E. Kalnay. Three years of operational prediction of forecast skill at NMC. Mon. Wea. Rev., 123:2132–2147, 1995.

[239] A. Yamaguchi, T. Ishihara, and Y. Fujino. Experimental study of the wind flow in a coastal region of Japan. J. of Wind Eng. & Ind. Aerodyn., 91:247–264, 2003.

[240] L. Yuancheng, F. Tingjian, and E. Yu. Short-term electrical load forecasting using least squares Support Vector Machines. IEEE Trans. on Power Syst., 17(3):626–632, August 2002.

[241] J. C. Zack. A new technique for short-term wind energy forecasting: a rapid update cycle with a physics-based atmospheric model. In CD-Proc. of the 2004 Global Wind power Conference, Chicago, Illinois (USA),March 2004.

[242] A. Zervos. Developing wind energy to meet the Kyoto targets in the European Union. Wind Energy, 6(3):309–319, July-September 2003.

[243] G. Zhang, Patuwo B. E., and M. Y. Hu. Forecasting with artificial neural networks: the state of the art. Int. J. Forecasting, 14(1):35–62, 1998.

[244] C. Ziehmann. Skill prediction of local weather forecasts based on the ECMWF ensemble. Nonlin. Proc. Geophys., 8:419–428, 2001.

Table des Matières

1 Introduction 1

1.1 General context

1.2 Forecasting wind power

1.3 Estimating the uncertainty of wind power forecasts

1.4 Purpose of the work

1.5 Structure of the thesis

2 State of the Art in Wind Power Forecasting

2.1 Introduction

2.2 Describing the basis of the problem.

2.2.1 The intermittent nature of wind generation

2.2.2 The various motivations for forecasting wind generation

2.2.3 The main aspects of the forecasting problem

2.3 Generic formulation of the wind power forecasting problem

2.4 The reference forecasting methods

2.5 The physical approaches

2.5.1 The physical methodology

2.5.2 Overview of physical methods

2.6 The statistical approaches

2.6.1 The statistical methodology

2.6.2 Overview of statistical methods

2.7 Conclusion

3 Characterizing the Uncertainty of Wind Power Predictions

3.1 Introduction

3.2 Defining and measuring forecast accuracy

3.2.1 The prediction error

3.2.2 Evaluation framework

3.2.3 Definition of an appropriate evaluation protocol

3.3 Scope of the study

3.3.1 Prediction methods

3.3.2 Case-studies

3.4 Evaluating the quality of state-of the-art point prediction methods

3.4.1 Analysis based on error measures

3.4.2 Performance against reference approaches

3.4.3 Analysis based on error distributions

3.5 Highlighting the characteristics of the prediction uncertainty

3.5.1 Contributions to the wind power prediction error

3.5.2 Characteristics of prediction errors

3.6 Conclusions

4 Estimation and Evaluation of Prediction Intervals of Wind Power

4.1 Introduction

4.2 Different types of statistical intervals

4.3 Basic parametric approaches for prediction interval estimation

4.4 Development of a distribution-free approach

4.4.1 Hypothesis and development of empirical-type methods

4.4.2 Classification of forecast conditions

4.4.3 The fuzzy inference model

4.4.4 Methods for combining error distributions

4.5 Application to the wind power forecasting problem

4.6 Discussion on operational aspects

4.7 A non-parametric framework for the evaluation of prediction intervals

4.7.1 Required properties for interval forecasts

4.7.2 Methods for the evaluation of prediction intervals

4.8 Result

4.8.1 Linear opinion pool vs. Adapted resampling

4.8.2 Influence of the fuzzy mapping of the forecast conditions

4.8.3 Influence of the sample size

4.8.4 Influence of the number of bootstrap replications

4.9 Conclusions

5 Ensemble Predictions of Wind Power for Skill Forecasting

5.1 Introduction

5.2 Ensemble predictions of wind power

5.2.1 The meteorological ensemble predictions from ECMWF and NCEP

5.2.2 Conversion to ensembles of wind power .

5.2.3 Poorman’s ensembles of wind power

5.3 Ensembles vs. spot forecasts

5.3.1 The possibility to derive more accurate point predictions

5.3.2 The ensembles’ ability to reflect the forecast uncertainty

5.4 Skill forecasts based on wind power ensembles

5.4.1 Skill forecasting in the wind power prediction literature

5.4.2 Definition of prediction risk indices

5.4.3 On the relation between NPRI and energy imbalance

5.4.4 Pointwise estimation of expected uncertainty

5.4.5 Estimation of the uncertainty for a look-ahead period

5.5 Conclusions

6 The Value of Forecasting and the Benefits from Uncertainty Estimation

6.1 Introduction

6.2 Trading wind generation in electricity markets

6.2.1 Describing the European electricity markets

6.2.2 Assumptions for the present study

6.2.3 Formulation of the problem

6.3 Definition of advanced bidding strategies

6.3.1 Point predictions as the best bids

6.3.2 Advanced bidding strategies based on probabilistic forecasts

6.4 Evaluation of bidding strategies on a European electricity pool

6.4.1 Specificities of the Dutch electricity market

6.4.2 Results and discussion

6.5 Conclusions

7 General Conclusions

7.1 Overall conclusions and contribution

7.2 Perspectives

A List of Publications

B Resume en français

B.1 Introduction

B.2 Etat de l’art de la prediction eolienne

B.3 Caracterisation de l’incertitude de prediction

B.4 Estimation et evaluation d’intervalles de prediction

B.5 Prediction ensemblistes et indices de risque

B.6 Valeur des predictions eoliennes et de l’information sur leur incertitude

B.7 Conclusions

C Implementation of an Online Module

D Point Forecasting Methods - Evaluation Results

D.1 Content description

D.2 Tunø Knob

D.3 Klim

D.4 Golagh

D.5 Sotavento

E Uncertainty Characteristics - Full Survey

E.1 Content description

E.2 Tunø Knob

E.3 Klim

E.4 Golagh

E.5 Sotavento

Bibliography

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