Solar photovoltaic power forecasting using optimized modified extreme learning machine technique

Manoja Kumar Behera1, Irani Majumder2, Niranjan Nayak1
1Department of Electrical and Electronics Engineering, SOA Deemed To Be University, Bhubaneswar 751030, India
2Department of Electrical Engineering, SOA Deemed To Be University, Bhubaneswar 751030, India

Tài liệu tham khảo

Sahu, 2015, A study on global solar PV energy developments and policies with special focus on the top ten solar PV power producing countries, Renew. Sustain. Energy Rev., 43, 621, 10.1016/j.rser.2014.11.058 Hosenuzzaman, 2015, Global prospects, progress, policies, and environmental impact of solar photovoltaic power generation, Renew. Sustain. Energy Rev., 41, 284, 10.1016/j.rser.2014.08.046 Steffel, 2012, Integrating solar generation on the electric distribution grid, IEEE Trans. Smart Grid, 3, 878, 10.1109/TSG.2012.2191985 Shah, 2015, A review of key power system stability challenges for large-scale PV integration, Renew. Sustain. Energy Rev., 41, 1423, 10.1016/j.rser.2014.09.027 Eftekharnejad, 2015, Optimal generation dispatch with high penetration of photovoltaic generation, IEEE Trans. Sustainable Energy, 6, 1013, 10.1109/TSTE.2014.2327122 Yang, 2014, A weather-based hybrid method for 1-day ahead hourly forecasting of PV power output, IEEE Trans. Sustainable Energy, 5, 917, 10.1109/TSTE.2014.2313600 Oudjana, S.H., et al., Short term photovoltaic power generation forecasting using neural network, in: IEEE 11th International Conference on Environment and Electrical Engineering (EEEIC), 2012, pp. 706–711. Bacher, 2009, Online short-term solar power forecasting, Sol. Energy, 83, 1772, 10.1016/j.solener.2009.05.016 Mellit, 2010, A 24-h forecast of solar irradiance using artificial neural network: Application for performance prediction of a grid-connected PV plant at Trieste Italy, Solar Energy., 84, 807, 10.1016/j.solener.2010.02.006 Sfetsos, 2000, Univariate and multivariate forecasting of hourly solar radiation with artificial intelligence techniques, Sol. Energy, 68, 169, 10.1016/S0038-092X(99)00064-X Perez, 2013, Comparison of numerical weather prediction solar irradiance forecasts in the US, Canada and Europe, Solar Energy, 94, 305, 10.1016/j.solener.2013.05.005 Geraldi, 2012, An advanced model for the estimation of the surface solar irradiance under all atmospheric conditions using MSG/SEVIRI data, IEEE Trans. Geosci. Remote Sens., 50, 2934, 10.1109/TGRS.2011.2178855 Benmouiza, 2013, Forecasting hourly global solar radiation using hybrid k-means and nonlinear autoregressive neural network models, Energy Convers. Manage., 75, 561, 10.1016/j.enconman.2013.07.003 Perez, 2010, Validation of short and medium term operational solar radiation forecasts in the US, Sol. Energy, 84, 2161, 10.1016/j.solener.2010.08.014 Wolff, 2016, Statistical learning for short-term photovoltaic power predictions, Comput. Sustain. Springer International Publishing, 645, 31 De Giorgi, 2014, Photovoltaic power forecasting using statistical methods: impact of weather data, IET Sci. Meas. Technol., 8, 90, 10.1049/iet-smt.2013.0135 Ruiz-Arias, 2010, Proposal of a regressive model for the hourly diffuse solar radiation under all sky conditions, Energy Convers. Manage., 51, 881, 10.1016/j.enconman.2009.11.024 Benghanem, 2007, A multiple correlation between different solar parameters in Medina, Saudi Arabia, Renew. Energy, 32, 2424, 10.1016/j.renene.2006.12.017 Kalogirou, 2001, Artificial neural networks in renewable energy systems applications: a review, Renew. Sustain. Energy Rev., 5, 373, 10.1016/S1364-0321(01)00006-5 Benmouiza, 2016, Small-scale solar radiation forecasting using ARMA and nonlinear autoregressive neural network models, Theor. Appl. Climatol., 124, 945, 10.1007/s00704-015-1469-z Voyant, 2017, Machine learning methods for solar radiation forecasting: a review, Renew. Energy, 105, 569, 10.1016/j.renene.2016.12.095 Zhifeng, 2014, PV power short-term forecasting model based on the data gathered from monitoring network, China Commun., 11, 61, 10.1109/CC.2014.7085385 Cao, 2012, Self-adaptive evolutionary extreme learning machine, Neural Process. Lett., 36, 285, 10.1007/s11063-012-9236-y Wan, 2015, Photovoltaic and solar power forecasting for smart grid energy management, CSEE J. Power Energy Syst, 1, 38, 10.17775/CSEEJPES.2015.00046 N. Pandiarajan, R. Muthu, Mathematical modeling of photovoltaic module with Simulink, in: 1st International Conference on Electrical Energy Systems (ICEES), 2011, pp. 258–263. Nguyen, 2015, Mathematical modeling of photovoltaic cell/module/arrays with tags in Matlab/Simulink, Environ. Syst. Res., 4, 24, 10.1186/s40068-015-0047-9 De Brito, 2013, Evaluation of the main MPPT techniques for photovoltaic applications, IEEE Trans. Ind. Electron., 60, 1156, 10.1109/TIE.2012.2198036 Liu, 2008, A variable step size INC MPPT method for PV systems, IEEE Trans. Ind. Electron., 55, 2622, 10.1109/TIE.2008.920550 Esram, 2007, Comparison of photovoltaic array maximum power point tracking techniques, IEEE Trans. Energy Convers., 22, 439, 10.1109/TEC.2006.874230 D.S. Karanjkar et al., Real time simulation and analysis of maximum power point tracking (MPPT) techniques for solar photo-voltaic system, Recent Adv. Eng. Comput. Sci. (RAECS) (2014) 1–6. Bizzarri, 2013, Model of photovoltaic power plants for performance analysis and production forecast, IEEE Trans. Sustain. Energy, 4, 278, 10.1109/TSTE.2012.2219563 Antonanzas, 2016, Review of photovoltaic power forecasting, Sol. Energy, 136, 78, 10.1016/j.solener.2016.06.069 Chen, 2012, Electricity price forecasting with extreme learning machine and bootstrapping, IEEE Trans. Power Syst., 27, 2055, 10.1109/TPWRS.2012.2190627 Raza, 2016, On recent advances in PV output power forecast, Sol. Energy, 136, 125, 10.1016/j.solener.2016.06.073 I. Abadi et al., Extreme learning machine approach to estimate hourly solar radiation on horizontal surface (PV) in Surabaya-East java, in: IEEE 1st International Conference on Information Technology, Computer and Electrical Engineering (ICITACEE) (2014) 372–376. Huang, 2004, Extreme learning machine: a new learning scheme of feedforward neural networks, IEEE Int. Joint Conf. Neural Networks, 2, 985 Huang, 2006, Extreme learning machine: theory and applications, Neurocomputing, 70, 489, 10.1016/j.neucom.2005.12.126 Ding, 2015, Extreme learning machine: algorithm, theory and applications, Artif. Intell. Rev., 44, 103, 10.1007/s10462-013-9405-z T.T. Teo et al., Forecasting of photovoltaic power using extreme learning machine, in: IEEE Innovative Smart Grid Technologies - Asia (ISGT ASIA) (2015) 1–6. Dehghan, 2016, Proper orthogonal decomposition variational multiscale element free Galerkin (POD-VMEFG) meshless method for solving incompressible Navier-Stokes equation, Comput. Methods Appl. Mech. Eng., 311, 856, 10.1016/j.cma.2016.09.008 Deng, 2010, Research on extreme learning of neural networks, Chin. J. Comput., 33, 279, 10.3724/SP.J.1016.2010.00279 Huang, 2015, Trends in extreme learning machines: a review., Neural Networks, 61, 32, 10.1016/j.neunet.2014.10.001 Q. Cheng et al., Short-term load forecasting with weather component based on improved extreme learning machine, in: Chinese Automation Congress (CAC), IEEE (2013) 316–321. E.M.N. Figueiredo, T.B. Ludermir, Effect of the PSO Topologies on the Performance of the PSO-ELM, in: Proceedings – Brazilian Symposium on Neural Networks (SBRN), IEEE (2012) 178–183. Zhu, 2005, Evolutionary extreme learning machine, Pattern Recognition, 38, 1759, 10.1016/j.patcog.2005.03.028 Han, 2013, An improved evolutionary extreme learning machine based on particle swarm optimization, Neurocomputing, 116, 87, 10.1016/j.neucom.2011.12.062 Kar, 2012, Craziness based Particle Swarm Optimization algorithm for FIR band stop filter design, Swarm Evol. Comput., 7, 58, 10.1016/j.swevo.2012.05.002 X.-S. Yang et al., Accelerated Particle Swarm Optimization and Support Vector Machine for Business Optimization and Applications, Networked Digital Technol. (2011) 53–66. Gandomi, 2013, Chaos-enhanced accelerated particle swarm optimization, Commun. Nonlinear Sci. Numer. Simul., 18, 327, 10.1016/j.cnsns.2012.07.017 Yang, 2010