Abstract
In this study, an artificial neural network was modeled in order to predict the power generated by a monocrystalline silicon photovoltaic panel. This experimental study measured and recorded the voltage and current generated by the photovoltaic panel for a year, along with environmental variables such as solar irradiance, air temperature, wind speed, wind direction, relative humidity, and angle of the sun’s elevation. In the results of the comparisons between measured and estimated power, a perfect estimation was found to have been conducted in which the root mean square error did not exceed 1.4% and the coefficient of correlation (R) ranged from 99.637 to 99.998%. These results were obtained from the testing dataset. In this study, achieved artificial neural network models are able to perform estimations for any location using the atmospheric indicators. These models are considered able to lead investors using extremely sensitive and robust estimations in order to learn solar energy’s potential in a location.
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Peidong Z, Yanli Y, Yonghong Z, Lisheng W, Xinrong L (2009) Opportunities and challenges for renewable energy policy in China. Renew Sust Energy Rev 13:439–449. https://doi.org/10.1016/j.rser.2007.11.005
Hrayshat ES (2007) Analysis of renewable energy situation in Jordan. Renew Sust Energy Rev 11:1873–1887. https://doi.org/10.1016/j.rser.2006.01.003
Kumar A, Kumar K, Kaushik N, Sharma S, Mishra S (2010) Renewable energy in India: current status and future potentials. Renew Sust Energy Rev 14:2434–2442. https://doi.org/10.1016/j.rser.2010.04.003
International Energy Agency (2014) Key world energy statistics. Paris, France
Dinçer F (2011) Overview of the photovoltaic technology status and perspective in Turkey. Renew Sust Energy Rev 15:3768–3779. https://doi.org/10.1016/j.rser.2011.06.005
Icli S, Colak M, Cubukcu M (2010) PV technology status and prospects. International Energy Association PPSP Annual Report Technology roadmap: solar photovoltaic energy. Fribourg, Switzerland, IEA Press, pp 118–120
International Energy Association (2014) Technology roadmap: solar photovoltaic energy. Paris, France
Bhandari R, Stadler I (2009) Grid parity analysis of solar photovoltaic systems in Germany using experience curves. Sol Energy 83:1634–1644. https://doi.org/10.1016/j.solener.2009.06.001
Panait MA, Tudorache TA (2008) A simple neural network solar tracker for optimizing conversion efficiency in off-grid solar generators. In: Proceedings of the ICREPQ’08, International Conference on Renewable Energies and Power Quality, Vigo, Spain, pp 156–160
Mellit A, Saglam S, Kalogirou SA (2013) Artificial neural network-based model for estimating the produced power of a photovoltaic module. Renew Energy 60:71–78. https://doi.org/10.1016/j.renene.2013.04.011
Pavan AM, Mellit A, De Pieri A, Lughi V (2014) A study on the mismatch effect due to the use of different photovoltaic modules classes in large-scale solar parks. Prog Photovolt Res Appl 22:332–345. https://doi.org/10.1002/pip.2266
Mekhilef S, Saidur R, Kamalisarvestani M (2012) Effect of dust, humidity and air velocity on efficiency of photovoltaic cells. Renew Sust Energy Rev 16:2920–2925. https://doi.org/10.1016/j.rser.2012.02.012
Hiyama T, Kitabayashi K (1997) Neural network based estimation of maximum power generation from PV module using environmental information. IEEE Trans Energy Conver 12:241–247. https://doi.org/10.1109/60.629709
Coelho A, Castro R (2012) Sun tracking PV power plants: experimental validation of irradiance and power output prediction models. Int J Renew Energy Res 2:23–32
Hontoria L, Aguilera J, Riesco J, Zufiria P (2001) Recurrent neural supervised models for generating solar radiation synthetic series. J Intell Robot Syst 31:201–221. https://doi.org/10.1023/A:1012031827871
Almonacid F, Rus C, Hontoria L, Fuentes M, Nofuentes G (2009) Characterisation of Si-crystalline PV modules by artificial neural networks. Renew Energy 34:941–949. https://doi.org/10.1016/j.renene.2008.06.010
Mellit A, Benghanem M, Kalogirou SA (2006) An adaptive wavelet-network model for forecasting daily total solar-radiation. Appl Energy 83:705–722. https://doi.org/10.1016/j.apenergy.2005.06.003
Mellit A, Benghanem M, Arab AH, Guessoum A (2005) 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. Sol Energy 79:469–482. https://doi.org/10.1016/j.solener.2004.12.006
Hiyama T, Kouzuma S, Imakubo T (1995) Identification of optimal operating point of PV modules using neural network for real time maximum power tracking control. IEEE Trans Energy Conver 10:360–367. https://doi.org/10.1109/60.391904
Hiyama T, Kouzuma S, Imakubo T, Ortmeyer TH (1995) Evaluation of neural network based real time maximum power tracking controller for PV system. IEEE Trans Energy Conver 10:543–548. https://doi.org/10.1109/60.464880
Veerachary M, Yadaiah N (2000) ANN based peak power tracking for PV supplied DC motors. Sol Energy 69:343–350. https://doi.org/10.1016/S0038-092X(00)00085-2
Bahgat ABG, Helwa NH, Ahamd GE, El Shenawy ET (2004) Estimation of the maximum power and normal operating power of a photovoltaic module by neural networks. Renew Energy 29:443–457. https://doi.org/10.1016/S0960-1481(03)00126-5
Bahgat ABG, Helwa NH, Ahamd GE, El Shenawy ET (2005) Maximum power point traking controller for PV systems using neural networks. Renew Energy 30:1257–1268. https://doi.org/10.1016/j.renene.2004.09.011
Mellit A, Benghanem M, Arab AH, Guessoum A (2005) An adaptive artificial neural network model for sizing stand-alone photovoltaic systems: application for isolated sites in Algeria. Renew Energy 30:1501–1524. https://doi.org/10.1016/j.renene.2004.11.012
Mellit A, Benghanem M, Kalogirou SA (2007) Modeling and simulation of a stand-alone photovoltaic system using an adaptive artificial neural network: proposition for a new sizing procedure. Renew Energy 32:285–313. https://doi.org/10.1016/j.renene.2006.01.002
Karatepe E, Boztepe M, Colak M (2006) Neural network based solar cell model. Energy Convers Manag 47:1159–1178. https://doi.org/10.1016/j.enconman.2005.07.007
Karatepe E, Hiyama T (2009) ANN based real-time estimation of power generation of different PV module types. IEEJ Trans Power Energy 129:783–790. https://doi.org/10.1541/ieejpes.129.783
AbdulHadi M, Al-Ibrahim AM, Virk GS (2004) Neuro-fuzzy-based solar cell model. IEEE Trans Energy Conver 19:619–624. https://doi.org/10.1109/TEC.2004.827033
Piliougine M, Elizondo D, Mora-López L, De-Cardona MS (2013) Photovoltaic module simulation by neural networks using solar spectral distribution. Prog Photovolt Res Appl 21:1222–1235. https://doi.org/10.1002/pip.2209
Kayri M (2015) An intelligent approach to educational data: performance comparison of the multilayer perceptron and the radial basis function artificial neural networks. Educ Sci Theory Pract 15:1247–1255. https://doi.org/10.12738/estp.2015.5.0238
Bui DT, Pradhan B, Lofman O, Revhaug I, Dick OB (2012) Landslide susceptibility assessment in the Hoa Binh province of Vietnam: a comparison of the Levenberg–Marquardt and Bayesian regularized neural networks. Geomorphology 171:12–29. https://doi.org/10.1016/j.geomorph.2012.04.023
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Kayri, I., Gencoglu, M.T. Predicting power production from a photovoltaic panel through artificial neural networks using atmospheric indicators. Neural Comput & Applic 31, 3573–3586 (2019). https://doi.org/10.1007/s00521-017-3271-6
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DOI: https://doi.org/10.1007/s00521-017-3271-6