A PSO-ANFIS based Hybrid Approach for Short Term PV Power Prediction in Microgrids

References

• T. L. Kottas, Y. S. Boutalis, and A. D. Karlis, “New maximum power point tracker for PV arrays using fuzzy controller in close cooperation fuzzy cognitive networks”, IEEE Trans. Energy Convers., vol. 21, no. 3, pp. 793–803, 2006. doi:10.1109/TEC.2006.875430.
   Web of Science ®Google Scholar
• M. Oliver and T. Jackson, “The market for solar photovoltaics”, Energy Pol., vol. 27, pp. 371–385, 1999. doi:10.1016/S0301-4215(99)00038-5.
   Web of Science ®Google Scholar
• M. Zamo, O. Mestre, P. Arbogast and O. Pannekoucke, “A benchmark of statistical regression methods for short-term forecasting of photovoltaic electricity production, part I: Deterministic forecast of hourly production”, Sol. Energy, vol. 105, pp. 792–803, 2014. doi:10.1016/j.solener.2013.12.006.
   Web of Science ®Google Scholar
• A. Azadeh, A. Maghsoudi, and S. Sohrabkhani, “An integrated artificial neural networks approach for predicting global radiation”, Energy Convers. Manage., vol. 50, no. 6, pp. 1497–1505, 2009. doi:10.1016/j.enconman.2009.02.019.
   Web of Science ®Google Scholar
• 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, 2011. doi:10.1016/j.solener.2011.01.007.
   Web of Science ®Google Scholar
• A. Mellit, and A. Massi 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, 2010. doi:10.1016/j.solener.2010.02.006.
   Web of Science ®Google Scholar
• L. Nonnenmacher, A. Kaur, and C. F.M. Coimbra, “Day-ahead resource forecasting for concentrated solar power integration”, Renewable Energy, vol. 86, pp. 866–876, 2016. doi:10.1016/j.renene.2015.08.068.
   Web of Science ®Google Scholar
• R. Perez, S. Kivalov, J. Schlemmer, K. Hemker Jr., D. Renne, and T. E. Hoff, “Validation of short and medium term operation solar radiation forecasts in the US,” Sol. Energy, vol. 84, pp. 2161–2172, 2010. doi:10.1016/j.solener.2010.08.014.
   Web of Science ®Google Scholar
• F. Wang, Z. Mi, S. Su, and H. Zhao, “Short-term solar irradiance forecasting model based on artificial neural network using statistical feature parameters,” Energies, vol. 5, pp. 1355–1370, 2012. doi:10.3390/en5051355.
   Web of Science ®Google Scholar
• C. W. Chow, B. Urquhart, M. Lave, A. Dominguez, J. Kleissl, J. Shields, and B. Washom, “Intra-hour forecasting with a total sky imager at the UC San Diego solar energy tested,” Sol. Energy, vol. 85, no. 11, pp. 2881–2893, 2011. doi:10.1016/j.solener.2011.08.025.
   Web of Science ®Google Scholar
• D. P. Larson, L. Nonnenmacher, and C. F. M. Coimbra, “Day-ahead forecasting of solar power output from photovoltaic plants in the American Southwest,” Renewable Energy, vol. 91, pp. 11–20, 2016. doi:10.1016/j.renene.2016.01.039.
   Web of Science ®Google Scholar
• P. Bacher, H. Madsen, and H. Nielsen, “Online short-term solar power forecasting,” Sol. Energy, vol. 83, pp. 1772–1783, 2009. doi:10.1016/j.solener.2009.05.016.
   Web of Science ®Google Scholar
• Y. Li, Y. Su, and L. Shu, “An ARMAX model for forecasting the power output of a grid connected photovoltaic system,” Renewable Energy, vol. 66, pp. 78–89, 2014. doi:10.1016/j.renene.2013.11.067.
   Web of Science ®Google Scholar
• Y. Chu, B. Urguhart, S. Gohari, H. Pedro, J. Kleissl, and C. Coimbra, “Short-term reforecasting of power output from a 48MWe solar PV plant,” Sol. Energy, vol. 112, pp. 68–77, 2015. doi:10.1016/j.solener.2014.11.017.
   Web of Science ®Google Scholar
• F. Almonacid, P. Perez-Higueras, E. Fernandez, and L. Hontoria, “A methodology based on dynamic artificial neural network for short-term forecasting of the power output of a PV generator,” Energy Convers. Manage., vol. 85, pp. 389–398, 2014. doi:10.1016/j.enconman.2014.05.090.
   Web of Science ®Google Scholar
• Y. Kashyap, A. Bansal, and A. K. Sao, “Solar radiation forecasting with multiple parameters neural networks,” Renewable Sustainable Energy Rev., vol. 49, pp. 825–835, 2015. doi:10.1016/j.rser.2015.04.077.
   Web of Science ®Google Scholar
• M. Rana, I. Koprinska, and V. Agelidis, “2D-interval forecasts for solar power production,” Sol. Energy, vol. 122, pp. 191–203, 2015. doi:10.1016/j.solener.2015.08.018.
   Web of Science ®Google Scholar
• A. Mellit, and S. A. Kalogirou, “ANFIS-based modelling for photovoltaic power supply system: A case study,” Renewable Energy, vol. 36, no. 1, pp. 250–256, 2015. doi:10.1016/j.renene.2010.06.028.
   Google Scholar
• R. Chauvin, J. Nou, S. Thil, and S. Grieu, “Intra-day DNI forecasting under clear sky conditions using ANFIS”, Proceedings of the 19th World Congress The International Federation of Automatic Control Cape Town, South Africa, August, 2014.
   Google Scholar
• M. Bouzerdoum, A. Mellit, and M. A. Pavan, “A hybrid model (SARIMA-SVM) for short-term power forecasting of a small-scale grid-connected photovoltaic plant,” Sol. Energy, vol. 98, pp. 226–235, 2013. doi:10.1016/j.solener.2013.10.002.
   Web of Science ®Google Scholar
• A. Vaz, B. Elsinga, W. V. Sark, and M. Brito, “An artificial neural network to assess the impact of neighbouring photovoltaic systems in power forecasting in Utrecht, the Netherlands,” Renewable Energy, vol. 85, pp. 631–641, 2016. doi:10.1016/j.renene.2015.06.061.
   Web of Science ®Google Scholar
• K.-P. Lin, and P.-F. Pai, “Solar power output forecasting using evolutionary seasonal decomposition least-square support vector regression,” J. Cleaner Product., 2015. http://dx.doi.org/10.1016/j.jclepro.2015.08.099.
   Web of Science ®Google Scholar
• J. Antonanzas, N. Osorio, R. Escobar, R. Urraca, F. J. Martinez-de-Pison, and F. Antonanzas-Torres, “Review of photovoltaic power forecasting,” Sol. Energy, vol. 136, pp. 78–111, 2016. doi:10.1016/j.solener.2016.06.069.
   Web of Science ®Google Scholar
• Ye Ren, P. N. Suganthan, and N. Srikanth, “Ensemble methods for wind and solar power forecasting-A state-of-the-art review,” Renewable Sustainable Energy Rev., vol. 50, pp. 82–91, 2015. doi:10.1016/j.rser.2015.04.081.
   Web of Science ®Google Scholar
• R. H. Inman, H. T. C. Pedro, and C. F.M. Coimbra, “Solar forecasting methods for renewable energy integration,” Prog. Energy Combust. Sci., vol. 39, pp. 535–576, 2013. doi:10.1016/j.pecs.2013.06.002.
   Web of Science ®Google Scholar
• J. Kennedy, and R. C. Ebenhart, “Particle swarm optimization”, Proceedings of the IEEE International Conference on neural networks, vol. 4, pp. 1942–1948, 1995.
   Google Scholar
• J. P. S Catalao, H. M. I. Pousinho, and V. M. F Mendes, “Hybrid wavelet-PSO-ANFIS approach for short-term wind power forecasting in Portugal,” IEEE Trans. Sustainable Energy, vol. 2, no. 1, pp. 50–59, 2011.
   Web of Science ®Google Scholar
• W. Yu, and X. Li, “Fuzzy identification using fuzzy neural networks with stable learning algorithms,” IEEE Trans. Fuzzy Syst., vol. 12, pp. 411–420, 2004. doi:10.1109/TFUZZ.2004.825067.
   Web of Science ®Google Scholar
• J. Kennedy. The behavior of particles. In V. W. Porto, N. Saravanan, D. Waagen, and A. E. Eiben, Eds. Evolutionary Programming VII: Proceedings of 7th Annual Conference on Evolutionary Programming Conf., San Diego, CA, pp. 581–589. Berlin: Springer-Verlag, 1998.
   Google Scholar
• J.-S. R. JANG, C. T. Sun, and E. Mizutani, Neuro-fuzzy and soft computing, A computational approach to learning and machine intelligence, New Jersey: Prentice Hall, 1997, pp. 73, 74, 86, 95–97, 86–87, 26–28, 74–85.
   Google Scholar
• J.-S.R. JANG, “ANFIS: adaptive network-based fuzzy inference systems,” IEEE Trans. Syst., Man Cybern., vol. 23, no. 3, pp. 665–685, 1993. doi:10.1109/21.256541.
   Web of Science ®Google Scholar
• V. S. Ghomsheh, M. A. Shoorehdeli, and M. Teshnehlab, “Training ANFIS structure with modified PSO algorithm,” 15th Mediterranean Conference on Control and Automation, July, 2007.
   Google Scholar
• Y. Kassa, J. H. Zhang, D. H. Zheng, and D. Wei, “Short term wind power prediction using ANFIS,” IEEE Int. Conf. Power Renewable Energy, pp. 388–393, 2016.
   Google Scholar