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Bibliography / References

  1. Bacher, Peder, “Online Short-term Solar Power Forecasting”, Solar Energy 83, 1772-1783 Accessed in September 2013.

  2.  Neves, Ricardo, “Desenvolvimento de modelos de previsão de produção de centrais fotovoltaicas”. Accessed in September 2013.

  3.  http://www.ntu.edu.sg/home/egbhuang/ , Accessed in September 2013

  4. http://cleantechnica.com/2013/10/08/advantages-disadvantages-solar-power/ , Accessed in October 2013

  5. http://www.erse.pt , Accessed in October 2013

  6. http://expresso.sapo.pt/renovaveis-abastecem-portugal-de-eletricidade=f797507, Accessed in October 2013

  7. http://www.indexmundi.com/blog/index.php/2013/08/07/china-at-the-top-of-renewable-energy-investment/ October 2013

  8. http://www.carbonbrief.org/blog/2013/01/renewable-investment-drops-2012/, Accessed in October 2013

  9. http://cleantechnica.com/2013/07/12/solar-pv-to-hit-grid-parity-134-billion-annual-revenue-by-2020/, Accessed in October 2013

  10. http://www.3tier.com/en/support/resource-maps/, Accessed in October 2013

  11. Monteiro, Cláudio, “Previsão de Consumos – O problema da previsão de consumos”, Available in http://paginas.fe.up.pt/~cdm/DE2/DE2_A3a.pdf , Accessed in October 2013

  12. Sousa, João, http://www.slideshare.net/CongressoEnergiaViana/joo-sousa-prewind , Accessed in October 2013

  13. http://www.cmhc-schl.gc.ca/en/co/grho/grho_009.cfm , Accessed in October 2013

  14. http://www.fsec.ucf.edu/en/consumer/solar_electricity/basics/how_pv_system_works.htm , Accessed in October 2013

  15. Centro de Referência para Energia Solar e Eólica Sérgio de Salvo Brito, "Energia Solar - Princípios e Aplicações"., Accessed in October 2013

  16. “Energia Fotovoltaica – Manual sobre tecnologias”, projecto e instalação, http://www.jgduarte.com/download/greenpro_fotovoltaico.pdf, Accessed in October 2013

  17. http://www.yourhome.gov.au/technical/fs67.html, Accessed in October 2013

  18. Hyndman, Rob J., Athanasopoulos, George, “Forecasting: Principles and practice”, http://www.otexts.org/book/fpp, Accessed in October 2013

  19. Sfetsos, A., Coonick, A.H., “Univariate and Multivariate Forecasting of Hourly Solar Radiation With Artificial Intelligence Techniques”, Solar Energy, 2000. 68(2) p.129-134, Accessed in November 2013

  20. Box, George E.P.; Jenkins, Gwimly M.; Reinsel, Gregory C., “Time Series Analysis: Forecasting Control”, Prentice-Hall Inc., Accessed in November 2013

  21. Heineman, Detlev; Lorenz, Elke; Girodo, Marco; “Forecasting of Solar Radiation”, Solar Energy 67, 139-150, Accessed in November 2013

  22. Wu, Ji; Chan, Chee Keong; “Prediction  of hourly solar radiation using a novel hybrid model of ARMA and TDNN”, Accessed in November 2013

  23. Heineman, Detlev; Lorenz, Elke; Girodo, Marco; “Solar irradiance forecasting for the management of solar energy systems”, Solar 2006, Denver, CO, USA, Accessed in November 2013

  24. Silva, Carlos, “Desenvolvimento de uma metodologia e ferramentas para a previsão da produção elétrica em parques fotovoltaicos”, Accessed in November 2013

  25. Zhang, G et al.,”Forecasting with artificial neural networks”, Accessed in November 2013

  26. Krogh, Anders, “What are artificial neural networks”, Available in http://www.apl.jhu.edu/~przytyck/neural_net_primer.pdf, accessed in November 2013

  27. http://www.learnartificialneuralnetworks.com/ , Accessed in November 2013

  28. Aamodt, Rune, “Using artificial networks to forecast financial time series”, Available in http://www.diva-portal.org/smash/get/diva2:353048/FULLTEXT01.pdf, Accessed in November 2013

  29. http://iticsoftware.com/forex-neural-backpropagation, Accessed in November 2013

  30. http://upload.wikimedia.org/wikipedia/commons/7/73/AtmosphericModelSchematic.png, Accessed in November 2013

  31. http://reference.wolfram.com/applications/neuralnetworks/NeuralNetworkTheory/2.5.1.html, Accessed in November 2013

  32. http://www.roguewave.com/portals/0/products/imsl-numerical-libraries/c-library/docs/7.0/html/cstat/default.htm?turl=multilayerfeedforwardneuralnetworks.htm, Accessed in November 2013

  33. Joachims, Thorsten, “Text categorization with support vector machines: Learning with many relevant features”, Available in http://link.springer.com/chapter/10.1007/BFb0026683#page-1, Accessed in November 2013

  34. Basak, Debasish et al., “Support Vector Regression”, Available in http://pdf.aminer.org/000/260/306/position_control_of_ultrasonic_motor_using_support_vector_regression.pdf, Accessed in November 2013

  35. Hammer, A. et al., “Solar energy assessment using remote sensing techniques”, Remote Sensing of Environment 86, 423-432, 2003, Accessed in January 2013

  36. Huang,Guang-Bin, Siew, Chee-Kheong, ”Extreme learning Machine with Randomly Assigned RBF Kernels”, International Journal of Information Technology, Vol. 11, No. 1, pp: 16-24, 2005. Accessed in November 2013

  37. Huang, G.; Zhou, H.; Ding,X.; et al.; “Extreme Learning Machines for regression and multiclass classification.”, Accessed November 2013

  38. Liu, Quige; He, Qing; Shi, Zhongzhi; “Extreme Support Vector Machine Classifier”, Accessed in December 2013

  39. Huang, G.; Zhu, Q.; Siew, C.; “Extreme Learning Machine: Theory and applications”, Neurocomputing, Vol. 70, pp: 489-501, 2006 Accessed in December 2013

  40. Frénay, Benoît; Verleysen, Michel; “Using SVMs with randomized feature spaces an extreme learning approach”, Accessed in December 2013

  41. Suykens, J.A.K.; Vandewalle, J.; “Training multilayer perceptron classifiers based on a modified support vector method”, Accessed in December 2013

  42. Huang, G.; Zhou, H.; Ding,X.; “Optimization method based extreme learning machine for classification”,Accessed in December 2013

  43. Bartlett, P.L.; “ The sample complexity of pattern classification with neural networks: The size of the weights is more important than the size of the network”, Accessed in December 2013

  44. http://www.3tier.com/en/support/solar-prospecting-tools/what-global-horizontal-irradiance-solar-prospecting/, Accessed in December 2013

  45. http://pvpmc.org/modeling-steps/irradiance-and-weather-2/irradiance-and-insolation/global-horizontal-irradiance/, Accessed in December 2013

  46. http://www.brighton-webs.co.uk/energy/solar_horizontal_surface.aspx, Accessed in December 2013

  47. http://www.bom.gov.au/climate/austmaps/solar-radiation-glossary.shtml, Accessed in December 2013

  48. http://www.3tier.com/en/support/solar-prospecting-tools/what-direct-normal-irradiance-solar-prospecting/, Accessed in December 2013

  49. Dai, Aiguo, et al.; “Recent trends in cloudiness over the united states: A tale of monitoring inadequencies”, Available in http://www.cgd.ucar.edu/cas/adai/papers/Dai_etal_BAMS_Clouds.pdf, Accessed in December 2013

  50. Mantzari, Vassilili H.; Mantzaris, Dimitrios H.; “Solar Radiation: Cloudiness forecasting using a soft computing approach”, Accessed in December 2013

  51. http://pveducation.org/pvcdrom/properties-of-sunlight/elevation-angle, accessed in December 2013

  52. Soteris A, K., “Applications of artificial neural-networks for energy systems. Applied Energy”, Applied Energy, 2000. 67(1-2): p. 17-35, Accessed in December 2013

  53. Ogliari, E., et al., “Hybrid Predictive Models for Accurate Forecasting in PV Systems”, Accessed in December 2013

  54. Yona, A.; Senjyu, T.; Saber, A.Y.; Funabashi, T.; Sekine, H.; Kim, C.-H. “Application of Neural Network to One-day-ahead 24 hours Generating Power Forecasting for Photovoltaic System.”, Accessed in December 2013

  55. Zeng, J; Qiao, W; “Short-term solar power prediction using a support vector machine”, Accessed in December 2013

  56. Huang, G et al.; “Extreme Learning Machine: A New Learning Scheme of Feedforward Neural Networks”, International Joint Conference on Neural Networks, Vol. 2, pp: 985-990, 2004, Accessed in December 2013

  57. “The Moore-Penrose pseudo-inverse”, Available in http://robotics.caltech.edu/~jwb/courses/ME115/handouts/pseudo.pdf, Accessed in December 2013

  58. “Neural activation functions”, Available in http://staff.science.uva.nl/~leo/math/sigma.pdf, Accessed in December 2013

  59. I.S.Isa, Z.Saad, S.Omar, M.K.Osman, K.A.Ahmad, H.A.Mat Sakim, “Suitable MLP Network Activation Functions for Breast Cancer and Thyroid Disease Detection”, Accessed in December 2013

  60. Bekir Karlik, A. Vehbi Olgac; “Performance Analysis of Various Activation Functions in Generalized MLP Architectures of Neural Networks”, Accessed in December 2013

  61. Muhammad Taher Abuelma'Att, Abdullah Bakri Shwehneh; "A Reconfigurable  Gaussian/triangular Basis Functions Computation Circuit”, Accessed in December 2013

  62. http://www.eumetcal.org/resources/ukmeteocal/verification/www/english/msg/ver_cont_var/uos3/uos3_ko1.htm, Accessed in December 2013

  63. B. H. Chowdhury and S. Rahman, "Forecasting sub-hourly solar irradiance for prediction of photovoltaic output", in Conference Record of the IEEE Photovoltaic Specialists Conference, pp. 171-176, (1987), Accessed in December 2013

  64. S. Hokoi, et al., "Stochastic models of solar radiation and outdoor temperature", in ASHRAE Transactions Vol. 2, pp.245-252, Accessed in December 2013

  65. Mathiesen and Kleissl, “Evaluation of numerical weather prediction for intra-day solar forecasting in the continental United States”, Solar Energy 85 (5), 967-977, Accessed in December 2013

  66. Claudio Monteiro et al., “Short-Term Power Forecasting Model for Photovoltaic Plants Based on Historical Similarity”, Accessed in December 2013~

  67. Diagne et al., “solar irradiation forecasting: state of the art and proposition for future developments for small-scale insular grids”, Accessed in December 2013

  68. Paoli et al., “ Forecasting of preprocessed daily solar radiation time series using neural networks”, Solar Energy 84 (12), 2146-2160 Accessed in December 2013

  69. Mellit, Adel; Pavan, A.M.; “A 24h forecast of solar irradiance using artificial neural network: Application of performance prediction of a grid-connected PV plant at Trieste, Italy”, Solar Energy 84 (5), 807-821, Accessed in December 2013

  70. Lorenz, E., et al.; “Irradiance forecasting for the power prediction of grid-connected photovoltaic system”, IEEE Journal of Selected Topics in Applied Earth Observations and remote sensing, Vol. 2, No. 1, March 2009, Accessed December 2013

  71. http://science.howstuffworks.com/dictionary/physics-terms/heat-info3.htm, Accessed in December 2013

  72. Kwok, J.T.Y.;”Support Vector Mixture for Classification and Regression Problems”, Accessed in December 2013

  73. Rajesh, R.; Prakash, J.S.;”Extreme Learning Machines – A Review and State-of-the-Art”, Accessed in December 2013

  74. Reikard, G.;”Predicting Solar Radiation at High Resolutions: A Comparison of Time Series Forecasts”, Solar Energy 83 (3), 342-349, Accessed in December 2013

  75. Hamilton, J.D.;”Time Series Analysis” Princeton University Press, 1994, Accessed in December 2013

  76. Remund et al.;”Comparison of solar radiation forecasts for the USA”, Solar Radiation at High Resolutions: A Comparison of Time Series Forecasts”, USA. Proc. of the 23rd                 European PV Conference, 1.9-4.9 2008, Valencia, Spain Accessed in December 2013

  77. Perez et al.;”Validation of short and medium term operational solar radiation forecasts in the US”, Solar Energy 84 (5) 2161-2172, Accessed in December 2013

  78. Cao and Cao;”Forecast of solar irradiance using recurrent neural networks combined with wavelet analysis”, Energy 31, 3435-3445 (2006), Accessed in December 2013

  79. Lorenz et al.;”Benchmarking of different approaches to forecast solar irradiance”, In: Proceedings of 24 European Photovoltaic and Solar Energy Conference and Exhibition, Hamburg (Germany), Accessed in December 2013

  80. http://www.altenergystocks.com/archives/2012/04/five_more_winners_of_the_clean_energy_race_1.html , Accessed in January 2014

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