Estimating the maximum power of a High Concentrator Photovoltaic (HCPV) module using an Artificial Neural Network
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[1] Douglas L. Maskell,et al. Estimation of dual-junction solar cell characteristics using neural networks , 2010, 2010 35th IEEE Photovoltaic Specialists Conference.
[2] K. Araki,et al. A simple rating method for CPV modules and systems , 2008, 2008 33rd IEEE Photovoltaic Specialists Conference.
[3] P.J. Verlinden,et al. Energy rating of Concentrator PV systems using multi-junction III–V solar cells , 2008, 2008 33rd IEEE Photovoltaic Specialists Conference.
[4] Pedro Pérez-Higueras,et al. Calculation of the energy provided by a PV generator. Comparative study: Conventional methods vs. artificial neural networks , 2011 .
[5] Takashi Hiyama,et al. Neural network based estimation of maximum power generation from PV module using environmental information , 1997 .
[6] M. Ranjan,et al. Solar resource estimation using artificial neural networks and comparison with other correlation models , 2003 .
[7] Jagdish C. Patra,et al. Neural network‐based model for dual‐junction solar cells , 2011 .
[8] N. H. Helwa,et al. Maximum power point traking controller for PV systems using neural networks , 2005 .
[9] Kenneth W. Stone,et al. Energy prediction of Amonix CPV solar power plants , 2011 .
[10] N. H. Helwa,et al. Estimation of the maximum power and normal operating power of a photovoltaic module by neural networks , 2004 .
[11] T. Fuyuki,et al. Annual output estimation of concentrator photovoltaic systems using high-efficiency InGaP/InGaAs/Ge triple-junction solar cells based on experimental solar cell's characteristics and field-test meteorological data , 2006 .
[12] Soteris A. Kalogirou,et al. MPPT-based artificial intelligence techniques for photovoltaic systems and its implementation into field programmable gate array chips: Review of current status and future perspectives , 2014 .
[13] Jorge Aguilera,et al. Generation of hourly irradiation synthetic series using the neural network multilayer perceptron , 2002 .
[14] Mummadi Veerachary,et al. ANN based peak power tracking for PV supplied dc motors , 2000 .
[15] S. Kurtz,et al. The influence of spectral solar irradiance variations on the performance of selected single-junction and multijunction solar cells , 1991 .
[16] Adel Mellit,et al. An adaptive artificial neural network model for sizing stand-alone photovoltaic systems: application for isolated sites in Algeria , 2005 .
[17] Hamid Taghavifar,et al. A comparative trend in forecasting ability of artificial neural networks and regressive support vector machine methodologies for energy dissipation modeling of off-road vehicles , 2014 .
[18] Joseph A. Jervase,et al. Solar radiation estimation using artificial neural networks , 2002 .
[19] J C Patra,et al. Chebyshev Neural Network-Based Model for Dual-Junction Solar Cells , 2011, IEEE Transactions on Energy Conversion.
[20] Marija Zlata Božnar,et al. Modelling Diffuse Solar Radiation in the City of SÃO Paulo Using Neural-Network Technique , 2004 .
[21] Mutlu Boztepe,et al. Neural network based solar cell model , 2006 .
[22] Douglas L. Maskell,et al. Modeling of multi-junction solar cells for estimation of EQE under influence of charged particles using artificial neural networks , 2012 .
[23] L. Wald,et al. On the clear sky model of the ESRA — European Solar Radiation Atlas — with respect to the heliosat method , 2000 .
[24] Adel Mellit,et al. A simplified model for generating sequences of global solar radiation data for isolated sites: Using artificial neural network and a library of Markov transition matrices approach , 2005 .
[25] Jorge Aguilera,et al. A tool for obtaining the LOLP curves for sizing off-grid photovoltaic systems based in neural networks , 2003, 3rd World Conference onPhotovoltaic Energy Conversion, 2003. Proceedings of.
[26] William E. Boyson,et al. Photovoltaic array performance model. , 2004 .
[27] Yingni Jiang,et al. Prediction of monthly mean daily diffuse solar radiation using artificial neural networks and comparison with other empirical models , 2008 .
[28] Manuel Fuentes,et al. Characterisation of Si-crystalline PV modules by artificial neural networks , 2009 .
[29] Gerald Siefer,et al. A method for using CPV modules as temperature sensors and its application to rating procedures , 2011 .
[30] C. Gueymard. Analysis of monthly average atmospheric precipitable water and turbidity in Canada and Northern United States , 1994 .
[31] Christian A. Gueymard,et al. Assessment of the Accuracy and Computing Speed of Simplified Saturation Vapor Equations Using a New Reference Dataset. , 1993 .
[32] Saleh M. Al-Alawi,et al. An ANN-based approach for predicting global radiation in locations with no direct measurement instrumentation , 1998 .
[33] K. Emery,et al. Spectral corrections based on optical air mass , 2002, Conference Record of the Twenty-Ninth IEEE Photovoltaic Specialists Conference, 2002..
[34] Tobias Gerstmaier,et al. Software Modeling of FLATCON© CPV Systems , 2010 .
[35] Eduardo F. Fernández,et al. Review of methods for the calculation of cell temperature in high concentration photovoltaic modules for electrical characterization , 2014 .
[36] D. Fadare. Modelling of solar energy potential in Nigeria using an artificial neural network model , 2009 .
[37] Soteris A. Kalogirou,et al. Artificial neural networks in renewable energy systems applications: a review , 2001 .
[38] Eduardo F. Fernández,et al. Relation between the cell temperature of a HCPV module and atmospheric parameters , 2012 .
[39] D. L. King,et al. Application and validation of a new PV performance characterization method , 1997, Conference Record of the Twenty Sixth IEEE Photovoltaic Specialists Conference - 1997.
[40] Soteris A. Kalogirou,et al. Artificial intelligence techniques for photovoltaic applications: A review , 2008 .
[41] Eduardo F. Fernandez,et al. Monolithic III-V triple-junction solar cells under different temperatures and spectra , 2011, Proceedings of the 8th Spanish Conference on Electron Devices, CDE'2011.
[42] E. Arcaklioğlu,et al. Use of artificial neural networks for mapping of solar potential in Turkey , 2004 .
[43] L. Hontoria,et al. Estimation of the energy of a PV generator using artificial neural network , 2009 .
[44] J. A. Gow,et al. Development of a photovoltaic array model for use in power-electronics simulation studies , 1999 .
[45] Georgios A. Florides,et al. Artificial neural networks for the generation of geothermal maps of ground temperature at various depths by considering land configuration , 2012 .
[46] Jorge Aguilera,et al. Recurrent Neural Supervised Models for Generating Solar Radiation Synthetic Series , 2001, J. Intell. Robotic Syst..
[47] Soteris A. Kalogirou,et al. Applications of artificial neural-networks for energy systems , 2000 .
[48] A. T. Young,et al. Revised optical air mass tables and approximation formula. , 1989, Applied optics.
[49] M. Meusel,et al. Influence of the simulator spectrum on the calibration of multi-junction solar cells under concentration , 2002, Conference Record of the Twenty-Ninth IEEE Photovoltaic Specialists Conference, 2002..
[50] A. García,et al. Selecting a suitable model for characterizing photovoltaic devices , 2002 .
[51] Hamid Taghavifar,et al. Applying a supervised ANN (artificial neural network) approach to the prognostication of driven wheel energy efficiency indices , 2014 .
[52] P. G. Vidal,et al. Outdoor evaluation of concentrator photovoltaic systems modules from different manufacturers: first results and steps , 2013 .
[53] Eduardo F. Fernández,et al. Quantifying the effect of air temperature in CPV modules under outdoor conditions , 2012 .
[54] Soteris A. Kalogirou,et al. Artificial neural networks for the prediction of the energy consumption of a passive solar building , 2000 .
[55] Eduardo F. Fernández,et al. Models for the electrical characterization of high concentration photovoltaic cells and modules: A review , 2013 .
[56] Manuel Fuentes,et al. Modelling the influence of atmospheric conditions on the outdoor real performance of a CPV (Concentrated Photovoltaic) module , 2014 .
[57] Tomonobu Senjyu,et al. Neural-network-based maximum-power-point tracking of coupled-inductor interleaved-boost-converter-supplied PV system using fuzzy controller , 2003, IEEE Trans. Ind. Electron..
[58] Eduardo F. Fernández,et al. Outdoor measurement of high concentration photovoltaic receivers operating with partial shading on the primary optics , 2013 .
[59] L. Zhang,et al. Application of radial basis function networks for solar-array modelling and maximum power-point prediction , 2000 .
[60] M. Shvarts,et al. On correct estimation of hourly power output of solar photovoltaic installations with MJ SCs and sunlight concentrators , 2008, 2008 33rd IEEE Photovoltaic Specialists Conference.
[61] Florencia Almonacid,et al. Spectrally corrected direct normal irradiance based on artificial neural networks for high concentrator photovoltaic applications , 2014 .
[62] Hamdy K. Elminir,et al. Estimation of solar radiation components incident on Helwan site using neural networks , 2005 .
[63] Soteris A. Kalogirou,et al. An adaptive wavelet-network model for forecasting daily total solar-radiation , 2006 .
[64] Marija Zlata Boznar,et al. Modeling hourly diffuse solar-radiation in the city of São Paulo using a neural-network technique , 2004 .
[65] B. Z. Adewole,et al. Artificial neural network prediction of exhaust emissions and flame temperature in LPG (liquefied petroleum gas) fueled low swirl burner , 2013 .
[66] S. C. Kaushik,et al. Assessment of diffuse solar energy under general sky condition using artificial neural network , 2009 .
[67] Adnan Sözen,et al. Estimation of solar potential in Turkey by artificial neural networks using meteorological and geographical data , 2004 .
[68] Soteris A. Kalogirou,et al. Modeling and simulation of a stand-alone photovoltaic system using an adaptive artificial neural network: Proposition for a new sizing procedure , 2007 .
[69] L. Hontoria,et al. Characterisation of PV CIS module by artificial neural networks. A comparative study with other methods , 2010 .