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REFERÊNCIA BIBLIOGRÁFICAS

[1] Associação de Energias Renováveis, “A Eletricidade de Origem Renovável em Portugal Continental Novembro de 2014,” 2014.

 

[2] M. T. L. Barros, F. T.-C. Tsai, S. Yang, J. E. G. Lopes, and W. W.-G. Yeh, “Optimization of Large-Scale Hydropower System Operations,” J. Water Resour. Plan. Manag., vol. 129, no. 3, pp. 178–188, 2003.

 

[3] F. Bessler, D. Savic, and G. Walters, “Water reservoir control with data mining,” J. Water Resour. Plan. Manag., vol. 129, no. 1, pp. 26–34, Jan. 2003.

 

[4] F. Botelho and N. Ganho, “Dinâmica anticiclónica subjacente à seca de 2004 / 2005 em Portugal Continental,” VI Semin. Lat. Am. Geogr. Física, pp. 1–14, 2010.

 

[5] P. Brandt, “MSBVAR: Markov-Switching, Bayesian, Vector Autoregression Models.” 2015.

 

[6] L. Breiman, “Bagging predictors,” Mach. Learn., vol. 24, no. 2, pp. 123–140, 1996.

 

[7] P. J. Brockwell and R. A. Davis, Introduction to Time Series and Forecasting, Second Edi. Taylor & Francis, 2002.

 

[8] F. S. Cabral, “A cheia milenar,” Público, 25-Feb-2008.

 

[9] A. J. Cannon, “Quantile regression neural networks: implementation in R and application to precipitation downscaling,” Comput. Geosci., vol. 37, pp. 1277–1284. doi:10.1016/j.cageo.2010.07.005, 2011.

 

[10] A. Cannon, “Essential Statistics,” The American Statistician, vol. 55, no. 1. Springer US, pp. 83–83, 2001.

 

[11] P. Chapman, J. Clinton, R. Kerber, T. Khabaza, T. Reinartz, C. Shearer, and R. Wirth, “CRISP-DM 1.0 Step-by-step data mining guide,” 2000. [Online]. Available: http://the-modeling-agency.com/crisp-dm.pdf. [Accessed: 05-Jan-2015].

 

[12] A. Coghlan, “Using R for Time Series Analysis,” 2010. [Online]. Available: http://a-little-book-of-r-for-time-series.readthedocs.org/en/latest/src/timeseries.html. [Accessed: 09-Jan-2015].

 

[13] J. D.Cryer and K.-S. Chan, Time Series Analysis With Applications in R, Second Edi. Springer, 2008.

 

[14] S. Despa, “Quantile Regression,” Cornell Univ. Cornell Stat. Consult. StatNews, no. 70, 2007.

 

[15] K. Diamantaras, W. Duch, and L. S. Iliadis, Artificial Neural Networks - ICANN 2010: 20th International Conference, Thessaloniki, Greece, September 15-18, 2010, Proceedings. Springer, 2010.

 

[16] EDP Produção - Departamento de Gestão da Operação, “Venda Nova: Normas de Exploração da Albufeira e Normas de Descarregamento,” (Documento Confidencial).

 

[17] EDP Produção - Departamento de Gestão da Operação, “Pocinho: Normas de Exploração da Albufeira e Normas de Descarregamento,” (Documento Confidencial).

 

[18] EDP Produção - Departamento de Gestão da Operação, “Alto Rabagão: Normas de Exploração da Albufeira e Normas de Descarregamento,” (Documento Confidencial).

 

[19] EDP Produção - Departamento de Gestão da Operação, “Salamonde: Normas de Exploração da Albufeira e Normas de Descarregamento,” (Documento Confidencial).

 

[20] EDP Produção - Departamento de Gestão da Operação, “Valeira: Normas de Exploração da Albufeira e Normas de Descarregamento,” (Documento Confidencial).

 

[21] C. Elkan, “The foundations of cost-sensitive learning,” in International joint conference on artificial intelligence, 2001, pp. 973–978.

 

[22] Entidade Reguladora dos Serviços Energéticos, “Envolvente de Mercado,” 2009. [Online]. Available: http://www.erse.pt/pt/supervisaodemercados/mercadodeelectricidade/envolventedemercado/Paginas/default.aspx?master=ErsePrint.master. [Accessed: 28-Jun-2015].

 

[23] J. Gattorna, Strategic Supply Chain Alignment: Best Practice in Supply Chain Management. Gower, 1998.

 

[24] G. Grothendieck, “dyn: Time Series Regression.” 2012.

 

[25] J. Han and M. Kamber, Data Mining: Concepts and Techniques, Second Edi. Elsevier, 2006.

 

[26] M. I. Hejazi and X. Cai, “Building more realistic reservoir optimization models using data mining – A case study of Shelbyville Reservoir,” Adv. Water Resour., vol. 34, no. 6, pp. 701–717, Jun. 2011.

 

[27] M. I. Hejazi, X. Cai, and B. L. Ruddell, “The role of hydrologic information in reservoir operation – Learning from historical releases,” Adv. Water Resour., vol. 31, no. 12, pp. 1636–1650, Dec. 2008.

 

[28] T. Hothorn, P. Buehlmann, S. Dudoit, A. Molinaro, and M. Van Der Laan, “Survival Ensembles,” Biostatistics, vol. 7, no. 3, pp. 355–373, 2006.

 

[29] W. Ishak, “Modelling Reservoir Water Release Decision Using Temporal Data Mining and Neural Network,” Int. J. Emerg. Technol. Adv. Eng., vol. 2, no. 8, pp. 422–428, 2012.

 

[30] H. I. Jager and B. T. Smith, “Sustainable reservoir operation: can we generate hydropower and preserve ecosystem values?,” River Res. Appl., vol. 24, no. 3, pp. 340–352, 2008.

 

[31] R. Koenker, “quantreg: Quantile Regression.” 2015.

 

[32] R. Koenker and K. F. Hallock, “Quantile Regression,” J. Econ. Perspect., vol. 15, no. 4, pp. 143–156, 2001.

 

[33] A. Liaw and M. Wiener, “Classification and Regression by randomForest,” R News, vol. 2, no. 3, pp. 18–22, 2002.

 

[34] C. Madureira and V. Baptista, Hidroelectricidade em Portugal : memória e desafio. Lisboa: Rede Eléctrica Nacional, S.A., 2002.

 

[35] MathWorks, “var.” [Online]. Available: http://www.mathworks.com/help/matlab/ref/var.html. [Accessed: 10-Jun-2015].

 

[36] N. Meinshausen, “quantregForest: Quantile Regression Forests.” 2012.

 

[37] A. Mohan and P. Revesz, “Temporal data mining of uncertain water reservoir data,” in Proceedings of the Third ACM SIGSPATIAL International Workshop on Querying and Mining Uncertain Spatio-Temporal Data, 2012, pp. 10–17.

 

[38] A. Mohan and P. Z. Revesz, “Applications of spatio-temporal data mining to north platter river reservoirs,” in Proceedings of the 18th International Database Engineering & Applications Symposium, 2014, pp. 306–309.

 

[39] NCS Pearson, “First Quartile,” TutorVista. [Online]. Available: http://math.tutorvista.com/statistics/first-quartile.html. [Accessed: 12-Jun-2015].

 

[40] B. A. Olshausen, “Aliasing,” 2000.

 

[41] D. G. Pascual, Artificial Intelligence Tools: Decision Support Systems in Condition Monitoring and DIagnosis. CRC Press, 2015.

 

[42] C. Perlich, S. Rosset, R. D. Lawrence, and B. Zadrozny, “High-quantile modeling for customer wallet estimation and other applications,” in Proceedings of the 13th ACM SIGKDD international conference on Knowledge discovery and data mining, 2007, pp. 977–985.

 

[43] M. Pinto and N. Silva, “CURSO O & M HÍDRICO - Exploração em regime de telecomando.” Universidade EDP, 2013.

 

[44] Portal Energia, “Funcionamento da energia hidrica e Barragens Hidroelectricas,” 2008. [Online]. Available: http://www.portal-energia.com/funcionamento-da-energia-hidrica-barragens-hidroelectricas/. [Accessed: 24-Jan-2014].

 

[45] K. L. Priddy and P. E. Keller, Artificial Neural Networks: An Introduction. SPIE Press, 2005.

 

[46] C. Ramos, M. Leal, and P. Silva, “Impactes das barragens nos regimes fluviais: comparação entre Vilarinho das Furnas (Hidroeléctrica) e Monte Novo (Hidroagrícola),” Trunfos uma Geogr. Act. Desenvolv. local, Ambient. Ordenam. e Tecnol., 2011.

 

[47] G. C. Reinsel, Elements of Multivariate Time Series Analysis. Springer New York, 2012.

 

[48] R. Rodrigues, C. Brandão, and J. P. da Costa, “Hidrologia das cheias do Mondego de 26 e 27 de Janeiro de 2001,” Relatório do Ina., 2001.

 

[49] R. Roiger and M. Geatz, Data Mining: A Tutorial-based Primer. Addison Wesley, 2003.

 

[50] R. H. Shumway and D. S. Stoffer, Time Series Analysis and Its Applications, Third edit., vol. 97. Springer Science & Business Media, 2011.

 

[51] M. Smithson and E. C. Merkle, Generalized Linear Models for Categorical and Continuous Limited Dependent Variables. Taylor & Francis, 2013.

 

[52] StatSoft Inc, “What is Data Mining (Predictive Analytics, Big Data),” 2015. [Online]. Available: http://www.statsoft.com/Textbook/Data-Mining-Techniques#mining. [Accessed: 01-Mar-2015].

 

[53] J. H. Stock, “Forecasting economic time series.,” A Companion to Theor. Econom., pp. 562–84, 1999.

 

[54] C. Stover and E. W. Weisstein, “Quantile,” MathWorld--A Wolfram Web Resource. [Online]. Available: http://mathworld.wolfram.com/Quantile.html. [Accessed: 12-Jun-2015].

 

[55] C. Strobl, A.-L. Boulesteix, T. Kneib, T. Augustin, and A. Zeileis, “Conditional Variable Importance for Random Forests,” BMC Bioinformatics, vol. 9, no. 307, 2008.

 

[56] C. Strobl, A.-L. Boulesteix, A. Zeileis, and T. Hothorn, “Bias in Random Forest Variable Importance Measures: Illustrations, Sources and a Solution,” BMC Bioinformatics, vol. 8, no. 25, 2007.

 

[57] R. Swinburne, Simplicity As Evidence of Truth, vol. 61. Marquette University Press, 1997.

 

[58] The Pennsylvania State University, “Vector Autoregressive models VAR(p) models,” STAT 510, 2015. [Online]. Available: https://onlinecourses.science.psu.edu/stat510/node/79. [Accessed: 03-Jun-2015].

 

[59] The Pennsylvania State University, “Moving Average Models (MA models),” STAT 510, 2015. [Online]. Available: https://onlinecourses.science.psu.edu/stat510/node/48. [Accessed: 03-Jun-2015].

 

[60] L. Torgo and R. Ribeiro, “Predicting rare extreme values,” in Advances in Knowledge Discovery and Data Mining, Springer, 2006, pp. 816–820.

 

[61] L. Torgo, R. Ribeiro, B. Pfahringer, and P. Branco, “SMOTE for Regression,” in Progress in Artificial Intelligence, L. Correia, L. P. Reis, and J. Cascalho, Eds. Springer Berlin Heidelberg, 2013, pp. 378–389.

 

[62] R. S. Tsay, “MTS: All-Purpose Toolkit for Analyzing Multivariate Time Series (MTS) and Estimating Multivariate Volatility Models.” 2015.

 

[63] S. Vedula and P. Mujumdar, “Optimal reservoir operation for irrigation of multiple crops,” Water Resour. Res., vol. 28, no. 1, pp. 1–9, 1992.

 

[64] W. N. Venables and B. D. Ripley, Modern Applied Statistics with S, Fourth. New York: Springer, 2002.

 

[65] G. Xu, Y. Zong, and Z. Yang, Applied Data Mining. CRC Press, 2013.

 

[66] Yale University, “Scatterplot,” Department of Statistics. [Online]. Available: http://www.stat.yale.edu/Courses/1997-98/101/scatter.htm. [Accessed: 17-Jun-2015].

 

[67] H. Yu and N. A. A. Rahim, Imaging in Cellular and Tissue Engineering. Taylor & Francis, 2013.

 

[68] C. Zhang and Y. Ma, Ensemble Machine Learning: Methods and Applications. Springer, 2012.

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