Bibliography
[1] G. Masson, M. Latour, M. Rekinger, I.-T. Theologitis, and M. Papoutsi, “Global Market Outlook.” 2013.
[2] P. L. Monteiro, “Projeto InovGrid,” Renováveis Magazine, pp. 50–53, 2012.
[3] C. Monteiro, R. Bessa, V. Miranda, A. Botterud, J. Wang, and G. Conzelmann, “Wind power forecasting: state-of-the-art 2009.” 2009.
[4] R. Neves, “Desenvolvimento de Modelos de Previsão de Produção de Centrais Solares Fotovoltaicas,” FEUP, 2010.
[5] L. Fernandez-Jimenez, A. Muñoz-Jimenez, A. Falces, M. Mendoza-Villena, E. Garcia-Garrido, P. M. Lara-Santillan, E. Zorzano-Alba, and P. J. Zorzano-Santamaria, “Short-term power forecasting system for photovoltaic plants,” Renew. Energy, vol. 44, pp. 311–317, Aug. 2012.
[6] E. Lorenz, J. Remund, S. C. Müller, W. Traunmüller, G. Steinmaurer, D. Pozo, J. Antonio, V. L. Fanego, L. Ramirez, M. G. Romeo, C. Kurz, L. M. Pomares, and C. G. Guerrero, “Benchmarking of different approaches to forecast solar irradiance.” .
[7] J. Remund, R. Perez, and E. Lorenz, “Comparison of solar radiation forecasts for the USA,” Eur. PV Conf., vol. 2, pp. 3–5, 2008.
[8] R. Perez, M. Beauharnois, Karl Hemker Jr., S. Kivalov, E. Lorenz, S. Pelland, J. Schlemmer, and G. Van Knowe, “Evaluation of numerical weather prediction solar irradiance forecasts in the US,” in American Solar Energy Society – Proc. ASES Annual Conference, 2011.
[9] R. Zamora, E. Dutton, M. Mckeen, J. Wilczak, and Y.-T. Hou, “The accuracy of solar irradiance calculations used in mesoscale numerical weather prediction,” Mon. Weather Rev., vol. 133, pp. 783–792, 2005.
[10] P. Bacher, H. Madsen, B. Perers, and H. A. Nielsen, “A non-parametric method for correction of global radiation observations,” Sol. Energy, vol. 88, pp. 13–22, Feb. 2013.
[11] H. Ohtake, K. Shimose, J. G. D. S. Fonseca, T. Takashima, T. Oozeki, and Y. Yamada, “Accuracy of the solar irradiance forecasts of the Japan Meteorological Agency mesoscale model for the Kanto region, Japan,” Sol. Energy, vol. 98, pp. 138–152, Dec. 2013.
[12] M. Wittmann, H. Breitkreuz, M. Schroedter-Homscheidt, and M. Eck, “Case Studies on the Use of Solar Irradiance Forecast for Optimized Operation Strategies of Solar Thermal Power Plants,” IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens., vol. 1, no. 1, pp. 18–27, Mar. 2008.
[13] A. Hammer, D. Heinemann, C. Hoyer, R. Kuhlemann, E. Lorenz, R. Müller, and H. G. Beyer, “Solar energy assessment using remote sensing technologies,” Remote Sens. Enviorment, vol. 86, no. 3, pp. 423–432, Aug. 2003.
[14] M. Ahlstrom and J. Kankiewicz, “Perspective and understanding on solar power forecasting,” in Solar Power Forecasting, 2009.
[15] H. Diagne, M. David, P. Lauret, and J. Boland, “Solar Irradiation Forecasting: State-of-the-art and Proposition for Future Developments for Small-scale Insular Grids,” reuniwatt.com, pp. 1–8, 2012.
[16] P. Bacher, H. Madsen, and H. Nielsen, “Online short-term solar power forecasting,” Sol. Energy, vol. 83, no. 10, pp. 1772–1783, Oct. 2009.
[17] H. Pedro and C. Coimbra, “Assessment of forecasting techniques for solar power production with no exogenous inputs,” Sol. Energy, vol. 86, no. 7, pp. 2017–2028, Jul. 2012.
[18] A. Mellit and A. M. Pavan, “A 24-h forecast of solar irradiance using artificial neural network: Application for performance prediction of a grid-connected PV plant at Trieste, Italy,” Sol. Energy, vol. 84, no. 5, pp. 807–821, May 2010.
[19] J. Huang, M. Korolkiewicz, M. Agrawal, and J. Boland, “Forecasting solar radiation on an hourly time scale using a Coupled AutoRegressive and Dynamical System (CARDS) model,” Sol. Energy, vol. 87, pp. 136–149, Jan. 2013.
[20] W. Ji and K. C. Chee, “Prediction of hourly solar radiation using a novel hybrid model of ARMA and TDNN,” Sol. Energy, vol. 85, no. 5, pp. 808–817, May 2011.
[21] R. Marquez and C. F. M. Coimbra, “Forecasting of global and direct solar irradiance using stochastic learning methods, ground experiments and the NWS database,” Sol. Energy, vol. 85, no. 5, pp. 746–756, May 2011.
[22] C. Paoli, C. Voyant, M. Muselli, and M.-L. Nivet, “Forecasting of preprocessed daily solar radiation time series using neural networks,” Sol. Energy, vol. 84, no. 12, pp. 2146–2160, Dec. 2010.
[23] E. Lorenz, J. Hurka, D. Heinemann, and H. G. Beyer, “Irradiance Forecasting for the Power Prediction of Grid-Connected Photovoltaic Systems,” IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens., vol. 2, no. 1, pp. 2–10, Mar. 2009.
[24] E. Lorenz, T. Scheidsteger, J. Hurka, D. Heinemann, and C. Kurz, “Regional PV power prediction for improved grid integration,” Prog. Photovoltaics Res. Appl., vol. 19, no. September 2010, pp. 757–771, 2011.
[25] C. Chen, S. Duan, T. Cai, and B. Liu, “Online 24-h solar power forecasting based on weather type classification using artificial neural network,” Sol. Energy, vol. 85, no. 11, pp. 2856–2870, Nov. 2011.
[26] A. Sfetsos and A. H. Coonick, “Univariate and Multivariate Forecasting of Hourly Solar Radiation with Artificial Intelligence Techniques,” Sol. Energy, vol. 68, no. 2, pp. 169–178, 2000.
[27] C. Silva, “Desenvolvimento de uma metodologia e ferramentas para a previsão da produção elétrica em parques fotovoltaicos,” FEUP, 2012.
[28] V. Berdugo, C. Chaussin, L. Dubus, G. Hebrail, and V. Leboucher, “Analog Method for Collaborative Very-Short-Term Forecasting of Power Generation from Photovoltaic Systems,” kd2u.org.
[29] C. Yang and L. Xie, “A novel ARX-based multi-scale spatio-temporal solar power forecast model,” in North American Power Symposium (NAPS), 2012, 2012.
[30] H. Von Storch and F. Zwiers, Statistical analysis in climate research. CAMBRIDGE UNIVERSITY PRESS, 2001.
[31] P. Bacher, “Short-term solar power forecasting,” 2008.
[32] D. N. . Gujarati and D. C. Porter, Basic Econometrics, Fifth inte. New York: McGraw-Hill, 2009.
[33] A. Zellner, “An efficient method of estimating seemingly unrelated regressions and tests for aggregation bias,” J. Am. Stat. Assoc., vol. 57, no. 298, pp. 348–368, 1962.
[34] L. Ljung and T. Soderstrom, Theory and practice of recursive identification. London, England: The MIT Press, Cambridge, Massachusetts, 1983.
[35] R. Lutz and P. Buhlmann, “Boosting for high-multivariate responses in high-dimensional linear regression,” Stat. Sin., vol. 16, pp. 471–494, 2006.
[36] J. Friedman, “Greedy function approximation: a gradient boosting machine,” Ann. Stat., vol. 29, no. 5, pp. 1189–1232, 2001.
[37] P. Bühlmann, “Boosting for high-dimensional linear models,” Ann. Stat., vol. 34, no. 2, pp. 559–583, Apr. 2006.
[1] G. Masson, M. Latour, M. Rekinger, I.-T. Theologitis, and M. Papoutsi, “Global Market Outlook.” 2013.
[2] P. L. Monteiro, “Projeto InovGrid,” Renováveis Magazine, pp. 50–53, 2012.
[3] C. Monteiro, R. Bessa, V. Miranda, A. Botterud, J. Wang, and G. Conzelmann, “Wind power forecasting: state-of-the-art 2009.” 2009.
[4] R. Neves, “Desenvolvimento de Modelos de Previsão de Produção de Centrais Solares Fotovoltaicas,” FEUP, 2010.
[5] L. Fernandez-Jimenez, A. Muñoz-Jimenez, A. Falces, M. Mendoza-Villena, E. Garcia-Garrido, P. M. Lara-Santillan, E. Zorzano-Alba, and P. J. Zorzano-Santamaria, “Short-term power forecasting system for photovoltaic plants,” Renew. Energy, vol. 44, pp. 311–317, Aug. 2012.
[6] E. Lorenz, J. Remund, S. C. Müller, W. Traunmüller, G. Steinmaurer, D. Pozo, J. Antonio, V. L. Fanego, L. Ramirez, M. G. Romeo, C. Kurz, L. M. Pomares, and C. G. Guerrero, “Benchmarking of different approaches to forecast solar irradiance.” .
[7] J. Remund, R. Perez, and E. Lorenz, “Comparison of solar radiation forecasts for the USA,” Eur. PV Conf., vol. 2, pp. 3–5, 2008.
[8] R. Perez, M. Beauharnois, Karl Hemker Jr., S. Kivalov, E. Lorenz, S. Pelland, J. Schlemmer, and G. Van Knowe, “Evaluation of numerical weather prediction solar irradiance forecasts in the US,” in American Solar Energy Society – Proc. ASES Annual Conference, 2011.
[9] R. Zamora, E. Dutton, M. Mckeen, J. Wilczak, and Y.-T. Hou, “The accuracy of solar irradiance calculations used in mesoscale numerical weather prediction,” Mon. Weather Rev., vol. 133, pp. 783–792, 2005.
[10] P. Bacher, H. Madsen, B. Perers, and H. A. Nielsen, “A non-parametric method for correction of global radiation observations,” Sol. Energy, vol. 88, pp. 13–22, Feb. 2013.
[11] H. Ohtake, K. Shimose, J. G. D. S. Fonseca, T. Takashima, T. Oozeki, and Y. Yamada, “Accuracy of the solar irradiance forecasts of the Japan Meteorological Agency mesoscale model for the Kanto region, Japan,” Sol. Energy, vol. 98, pp. 138–152, Dec. 2013.
[12] M. Wittmann, H. Breitkreuz, M. Schroedter-Homscheidt, and M. Eck, “Case Studies on the Use of Solar Irradiance Forecast for Optimized Operation Strategies of Solar Thermal Power Plants,” IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens., vol. 1, no. 1, pp. 18–27, Mar. 2008.
[13] A. Hammer, D. Heinemann, C. Hoyer, R. Kuhlemann, E. Lorenz, R. Müller, and H. G. Beyer, “Solar energy assessment using remote sensing technologies,” Remote Sens. Enviorment, vol. 86, no. 3, pp. 423–432, Aug. 2003.
[14] M. Ahlstrom and J. Kankiewicz, “Perspective and understanding on solar power forecasting,” in Solar Power Forecasting, 2009.
[15] H. Diagne, M. David, P. Lauret, and J. Boland, “Solar Irradiation Forecasting: State-of-the-art and Proposition for Future Developments for Small-scale Insular Grids,” reuniwatt.com, pp. 1–8, 2012.
[16] P. Bacher, H. Madsen, and H. Nielsen, “Online short-term solar power forecasting,” Sol. Energy, vol. 83, no. 10, pp. 1772–1783, Oct. 2009.
[17] H. Pedro and C. Coimbra, “Assessment of forecasting techniques for solar power production with no exogenous inputs,” Sol. Energy, vol. 86, no. 7, pp. 2017–2028, Jul. 2012.
[18] A. Mellit and A. M. Pavan, “A 24-h forecast of solar irradiance using artificial neural network: Application for performance prediction of a grid-connected PV plant at Trieste, Italy,” Sol. Energy, vol. 84, no. 5, pp. 807–821, May 2010.
[19] J. Huang, M. Korolkiewicz, M. Agrawal, and J. Boland, “Forecasting solar radiation on an hourly time scale using a Coupled AutoRegressive and Dynamical System (CARDS) model,” Sol. Energy, vol. 87, pp. 136–149, Jan. 2013.
[20] W. Ji and K. C. Chee, “Prediction of hourly solar radiation using a novel hybrid model of ARMA and TDNN,” Sol. Energy, vol. 85, no. 5, pp. 808–817, May 2011.
[21] R. Marquez and C. F. M. Coimbra, “Forecasting of global and direct solar irradiance using stochastic learning methods, ground experiments and the NWS database,” Sol. Energy, vol. 85, no. 5, pp. 746–756, May 2011.
[22] C. Paoli, C. Voyant, M. Muselli, and M.-L. Nivet, “Forecasting of preprocessed daily solar radiation time series using neural networks,” Sol. Energy, vol. 84, no. 12, pp. 2146–2160, Dec. 2010.
[23] E. Lorenz, J. Hurka, D. Heinemann, and H. G. Beyer, “Irradiance Forecasting for the Power Prediction of Grid-Connected Photovoltaic Systems,” IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens., vol. 2, no. 1, pp. 2–10, Mar. 2009.
[24] E. Lorenz, T. Scheidsteger, J. Hurka, D. Heinemann, and C. Kurz, “Regional PV power prediction for improved grid integration,” Prog. Photovoltaics Res. Appl., vol. 19, no. September 2010, pp. 757–771, 2011.
[25] C. Chen, S. Duan, T. Cai, and B. Liu, “Online 24-h solar power forecasting based on weather type classification using artificial neural network,” Sol. Energy, vol. 85, no. 11, pp. 2856–2870, Nov. 2011.
[26] A. Sfetsos and A. H. Coonick, “Univariate and Multivariate Forecasting of Hourly Solar Radiation with Artificial Intelligence Techniques,” Sol. Energy, vol. 68, no. 2, pp. 169–178, 2000.
[27] C. Silva, “Desenvolvimento de uma metodologia e ferramentas para a previsão da produção elétrica em parques fotovoltaicos,” FEUP, 2012.
[28] V. Berdugo, C. Chaussin, L. Dubus, G. Hebrail, and V. Leboucher, “Analog Method for Collaborative Very-Short-Term Forecasting of Power Generation from Photovoltaic Systems,” kd2u.org.
[29] C. Yang and L. Xie, “A novel ARX-based multi-scale spatio-temporal solar power forecast model,” in North American Power Symposium (NAPS), 2012, 2012.
[30] H. Von Storch and F. Zwiers, Statistical analysis in climate research. CAMBRIDGE UNIVERSITY PRESS, 2001.
[31] P. Bacher, “Short-term solar power forecasting,” 2008.
[32] D. N. . Gujarati and D. C. Porter, Basic Econometrics, Fifth inte. New York: McGraw-Hill, 2009.
[33] A. Zellner, “An efficient method of estimating seemingly unrelated regressions and tests for aggregation bias,” J. Am. Stat. Assoc., vol. 57, no. 298, pp. 348–368, 1962.
[34] L. Ljung and T. Soderstrom, Theory and practice of recursive identification. London, England: The MIT Press, Cambridge, Massachusetts, 1983.
[35] R. Lutz and P. Buhlmann, “Boosting for high-multivariate responses in high-dimensional linear regression,” Stat. Sin., vol. 16, pp. 471–494, 2006.
[36] J. Friedman, “Greedy function approximation: a gradient boosting machine,” Ann. Stat., vol. 29, no. 5, pp. 1189–1232, 2001.
[37] P. Bühlmann, “Boosting for high-dimensional linear models,” Ann. Stat., vol. 34, no. 2, pp. 559–583, Apr. 2006.