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Research Article

Estimating the power generating of a stand-alone solar photovoltaic panel using artificial neural networks and statistical methods

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Pages 2496-2508 | Received 24 Aug 2020, Accepted 01 Nov 2020, Published online: 26 Nov 2020

References

  • Ashraf, I., and A. Chandra. 2004. Artificial neural network based models for forecasting electricity generation of grid connected solar PV power plant. International Journal of Global Energy Issues 21 (1–2):119–30. doi:10.1504/IJGEI.2004.004704.
  • Azadeh, A., R. Babazadeh, and S. M. Asadzadeh. 2013. Optimum estimation and forecasting of renewable energy consumption by artificial neural networks. Renewable and Sustainable Energy Reviews 27:605–12. doi:10.1016/j.rser.2013.07.007.
  • Çelik, Ö., A. Teke, and H. B. Yıldırım. 2016. The optimized artificial neural network model with Levenberg–Marquardt algorithm for global solar irradiance estimation in Eastern Mediterranean Region of Turkey. Journal of Cleaner Production 116:1–12. doi:10.1016/j.jclepro.2015.12.082.
  • Chou, J. S., and C. F. Tsai. 2012. Concrete compressive strength analysis using a combined classification and regression technique. Automation in Construction 24:52–60. doi:10.1016/j.autcon.2012.02.001.
  • Fumo, N., and M. R. Biswas. 2015. Regression analysis for prediction of residential energy consumption. Renewable and Sustainable Energy Reviews 47:332–43. doi:10.1016/j.rser.2015.03.035.
  • Gökmen, N., W. Hu, P. Hou, Z. Chen, D. Sera, and S. Spataru. 2016. Investigation of wind speed cooling effect on PV panels in windy locations. Renewable Energy 90:283–90. doi:10.1016/j.renene.2016.01.017.
  • Graditi, G., S. Ferlito, G. Adinolfi, G. M. Tina, and C. Ventura (2014, March). Performance estimation of a thin-film photovoltaic plant based on an artificial neural network model. In 2014 5th International Renewable Energy Congress (IREC), Hammamet (Tunisia), 1–6. Hammamet, Tunisia: IEEE.
  • Hanselman, D. C., and B. Littlefield. 2012. Mastering matlab. New Jersey: Pearson Prentice Hall.
  • Haykin, S. 1994. Neural networks: A comprehensive foundation. New York, NY: Macmillan.
  • Haykin, S. 2010. Neural networks and learning machines, 3/E. New Jersey: Pearson Prentice Hall.
  • Hiyama, T., and K. Kitabayashi. 1997. Neural network based estimation of maximum power generation from PV module using environmental information. IEEE Transactions on Energy Conversion 12 (3):241–47. doi:10.1109/60.629709.
  • Huang, C., A. Bensoussan, M. Edesess, and K. L. Tsui. 2016. Improvement in artificial neural network-based estimation of grid connected photovoltaic power output. Renewable Energy 97:838–48. doi:10.1016/j.renene.2016.06.043.
  • Humada, A. M., M. Hojabri, S. Mekhilef, and H. M. Hamada. 2016. Solar cell parameters extraction based on single and double-diode models: A review. Renewable and Sustainable Energy Reviews 56:494–509. doi:10.1016/j.rser.2015.11.051.
  • Icel, Y., M. S. Mamis, A. Bugutekin, and M. I. Gursoy. 2019. Photovoltaic panel efficiency estimation with artificial neural networks: samples of Adiyaman, Malatya, and Sanliurfa. International Journal of Photoenergy 2019:1–12.
  • IRENARE Capacity_Statistics_2020. Accessed September 24, 2020. https://www.irena.org/publications/2020/Mar/Renewable-Capacity-Statistics-2020.
  • Kalogirou, S. A. 2000. Applications of artificial neural-networks for energy systems. Applied Energy 67 (1–2):17–35. doi:10.1016/S0306-2619(00)00005-2.
  • Kaytez, F., M. C. Taplamacioglu, E. Cam, and F. Hardalac. 2015. Forecasting electricity consumption: A comparison of regression analysis, neural networks and least squares support vector machines. International Journal of Electrical Power & Energy Systems 67:431–38. doi:10.1016/j.ijepes.2014.12.036.
  • Khademi, F., S. M. Jamal, N. Deshpande, and S. Londhe. 2016. Predicting strength of recycled aggregate concrete using artificial neural network, adaptive neuro-fuzzy inference system and multiple linear regression. International Journal of Sustainable Built Environment 5 (2):355–69. doi:10.1016/j.ijsbe.2016.09.003.
  • Lewis, C. D. 1982. Industrial and business forecasting methods: A practical guide to exponential smoothing and curve fitting. London: Butterworth-Heinemann.
  • Lo Brano, V., G. Ciulla, and M. Di Falco. 2014. Artificial neural networks to predict the power output of a PV panel. International Journal of Photoenergy 2014:1–12.
  • Malik, P., and S. S. Chandel. 2020. A new integrated single‐diode solar cell model for photovoltaic power prediction with experimental validation under real outdoor conditions. International Journal of Energy Research. doi:10.1002/er.5881.
  • Nunnally, J. C. 1978. Psychometric theory. New York: McGraw-Hill.
  • Perveen, G., M. Rizwan, N. Goel, and P. Anand. 2020. Artificial neural network models for global solar energy and photovoltaic power forecasting over India. Energy Sources, Part A: Recovery, Utilization, and Environmental Effects 1–26. doi:10.1080/15567036.2020.1826017.
  • Rehman, S., and M. Mohandes. 2008. Artificial neural network estimation of global solar irradiance using air temperature and relative humidity. Energy Policy 36 (2):571–76. doi:10.1016/j.enpol.2007.09.033.
  • Rocha, A. S. F., F. K. D. O. M. V. Guerra, and M. R. B. G. Vale. 2020. Forecasting the performance of a photovoltaic solar system installed in other locations using artificial neural networks. In Electric Power Components and Systems 48:1–12, 201–212. UK: Taylor & Francis. doi:10.1080/15325008.2020.1736211.
  • Rus-Casas, C., J. D. Aguilar, P. Rodrigo, F. Almonacid, and P. J. Pérez-Higueras. 2014. Classification of methods for annual energy harvesting calculations of photovoltaic generators. Energy Conversion and Management 78:527–36. doi:10.1016/j.enconman.2013.11.006.
  • Sözen, A., and E. Arcaklioglu. 2007. Prediction of net energy consumption based on economic indicators (GNP and GDP) in Turkey. Energy Policy 35 (10):4981–92. doi:10.1016/j.enpol.2007.04.029.
  • Verma, D., S. Nema, A. M. Shandilya, and S. K. Dash. 2016. Maximum power point tracking (MPPT) techniques: Recapitulation in solar photovoltaic systems. Renewable and Sustainable Energy Reviews 54:1018–34. doi:10.1016/j.rser.2015.10.068.
  • Xydis, G. 2013. The wind chill temperature effect on a large‐scale PV plant—an exergy approach. Progress in Photovoltaics: Research and Applications 21 (8):1611–24. doi:10.1002/pip.2247.
  • Yahya-Khotbehsara, A., and A. Shahhoseini. 2018. A fast modeling of the double-diode model for PV modules using combined analytical and numerical approach. Solar Energy 162:403–09. doi:10.1016/j.solener.2018.01.047.
  • Yona, A., T. Senjyu, A. Saber, T. Funabashi, H. Sekine, and C. Kim. 2007. Application of neural network to one-day-ahead 24 hours generating power forecasting for photovoltaic system. 2007 International Conference On Intelligent Systems Applications To Power Systems, Niigata, Japan..

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