Solar Power Estimation Methods Using ANN and CA-ANN Models for Hydrogen Production Potential in Mediterranean Region

References

• V. Saxena, N. Kumar, B. Singh, and B. K. Panigrahi, “A voltage support control strategy for grid integrated solar PV system during abnormal grid conditions utilizing interweaved GI,” IEEE Trans. Ind. Electron., Vol. 68, no. 9, pp. 8149–8157, 2020. DOI:10.1109/TIE.2020.3013771.
   Web of Science ®Google Scholar
• V. Saxena, N. Kumar, B. Singh, and B. K. Panigrahi, “A rapid circle centre-line concept-based MPPT algorithm for solar photovoltaic energy conversion systems,” IEEE Trans. Circ. Syst. Regul. Pap., Vol. 68, no. 2, pp. 940–949, 2020. DOI:10.1109/TCSI.2020.3038114.
   Web of Science ®Google Scholar
• V. Saxena, N. Kumar, B. Singh, and B. K. Panigrahi, “A spontaneous control for grid integrated solar photovoltaic energy conversion systems with voltage profile considerations,” IEEE Trans. Sustain. Energy, Vol. 12, no. 4, pp. 2159–2168, 2021. DOI:10.1109/TSTE.2021.3084103.
   Web of Science ®Google Scholar
• V. Saxena, N. Kumar, B. Singh, and B. K. Panigrahi, “An MPC based algorithm for a multipurpose grid integrated solar PV system with enhanced power quality and PCC voltage assist,” IEEE Trans. Energy Convers., Vol. 36, no. 2, pp. 1469–1478, 2021. DOI:10.1109/TEC.2021.3059754.
   Web of Science ®Google Scholar
• V. Saxena, N. Kumar, B. Singh, and B. K. Panigrahi. “An enhanced multilayer GI based control for grid integrated solar PV system,” in 2020 IEEE International Conference on Power Electronics, Drives and Energy Systems (PEDES), Jaipur, India, Dec. 2020, pp. 1–6. DOI:10.1109/PEDES49360.2020.9379446.
   Google Scholar
• V. Saxena, N. Kumar, B. Singh, and B. K. Panigrahi. “Control scheme for single phase single stage solar pv energy conversion system,” in 2021 International Conference on Sustainable Energy and Future Electric Transportation (SEFET), Hyderabad, India, Jan. 2021, pp. 1–5. DOI:10.1109/SeFet48154.2021.9375821.
   Google Scholar
• O. Derse, E. Göçmen, E. Yılmaz, and R. Erol, “A mathematical programming model for facility location optimization of hydrogen production from renewable energy sources,” Energy Sourc. Part A Recov. Utiliz. Environ. Eff., 1–12, Aug. 2020. DOI:10.1080/15567036.2020.1812769.
   Web of Science ®Google Scholar
• D. Akroum-Amrouche, H. Akroum, and H. Lounici, “Green hydrogen production by Rhodobacter sphaeroides,” Energy Sourc. Part A Recov. Utiliz. Environ. Eff., 1–19, Sep. 2019. DOI:10.1080/15567036.2019.1666190.
   Web of Science ®Google Scholar
• M. Balat, and N. Ozdemir, “New and renewable hydrogen production processes,” Energy Sourc., Vol. 27, no. 13, pp. 1285–1298, Oct. 2005. DOI:10.1080/009083190519564.
   Web of Science ®Google Scholar
• A. Ursua, L. M. Gandia, and P. Sanchis, “Hydrogen production from water electrolysis: current status and future trends,” Proc. IEEE, Vol. 100, no. 2, pp. 410–426, Feb. 2012. DOI:10.1109/JPROC.2011.2156750.
   Web of Science ®Google Scholar
• C. A. Grimes, O. K. Varghese, and S. Ranjan, “Hydrogen generation by water splitting,” in Light, C. A. Grimes, O. K. Varghese, and S. Ranjan, Eds., Boston: Springer, 2008, pp. 35–113.
   Google Scholar
• J. D. Holladay, J. Hu, D. L. King, and Y. Wang, “An overview of hydrogen production technologies,” Catal. Today, Vol. 139, no. 4, pp. 244–260, Jan. 2009. DOI:10.1016/j.cattod.2008.08.039.
   Web of Science ®Google Scholar
• X. Wang, et al., “A metal-free polymeric photocatalyst for hydrogen production from water under visible light,” Nature Mater., Vol. 8, no. 1, pp. 76–80, Jan. 2009. DOI:10.1038/nmat2317.
   PubMed Web of Science ®Google Scholar
• O. Khaselev, and J. A. Turner, “A monolithic photovoltaic-photoelectrochemical device for hydrogen production via water splitting,” Science, Vol. 280, no. 5362, pp. 425–427, Apr. 1998. DOI:10.1126/science.280.5362.425.
   PubMed Web of Science ®Google Scholar
• O. Ulleberg, “Modeling of advanced alkaline electrolyzers: a system simulation approach,” Int. J. Hydrogen Energy, Vol. 28, no. 1, pp. 21–33, Jan. 2003. DOI:10.1016/S0360-3199(02)00033-2.
   Web of Science ®Google Scholar
• M. Maruf-ul-Karim, and M. T. Iqbal. “Dynamic modeling and simulation of alkaline type electrolyzers,” in 2009 Canadian Conference on Electrical and Computer Engineering, St. John”s, NL, Canada, May 2009, pp. 711–715. DOI: 10.1109/CCECE.2009.5090222.
   Google Scholar
• T. M. Ismail, K. Ramzy, B. E. Elnaghi, M. N. Abelwhab, and M. A. El-Salam, “Using MATLAB to model and simulate a photovoltaic system to produce hydrogen,” Energy Convers. Manage., Vol. 185, pp. 101–129, Apr. 2019. DOI:10.1016/j.enconman.2019.01.108.
   Web of Science ®Google Scholar
• J. A. Turner, “Sustainable hydrogen production,” Science, Vol. 305, no. 5686, pp. 972–974, Aug. 2004. DOI:10.1126/science.1103197.
   PubMed Web of Science ®Google Scholar
• P.-H. Huang, J.-K. Kuo, and Z.-D. Wu, “Applying small wind turbines and a photovoltaic system to facilitate electrolysis hydrogen production,” Int. J. Hydrogen Energy, Vol. 41, no. 20, pp. 8514–8524, Jun. 2016. DOI:10.1016/j.ijhydene.2016.02.051.
   Web of Science ®Google Scholar
• J. Turner, et al., “Renewable hydrogen production,” Int. J. Energy Res., Vol. 32, no. 5, pp. 379–407, Apr. 2008. DOI:10.1002/er.1372.
   Web of Science ®Google Scholar
• M. A. Al-Refai, “Matlab/simulink simulation of solar energy storage system,” Int. J. Electr. Comp. Eng., Vol. 8, no. 2, pp. 6, 2014.
   Google Scholar
• S. Sharifian, N. Asasian Kolur, and M. Harasek, “Transient simulation and modeling of photovoltaic-PEM water electrolysis,” Energy Sourc. Part A Recov. Utiliz. Environ. Eff., Vol. 42, no. 9, pp. 1097–1107, May 2020. DOI:10.1080/15567036.2019.1602220.
   Google Scholar
• M. Sahin, and H. Okumus, “Fuzzy logic controlled parallel connected synchronous buck DC-DC converter for water electrolysis,” IETE J. Res., Vol. 59, no. 3, pp. 280, 2013. DOI:10.4103/0377-2063.116093.
   Web of Science ®Google Scholar
• B. D. Mert, F. Ekinci, and T. Demirdelen, “Effect of partial shading conditions on off-grid solar PV/hydrogen production in high solar energy index regions,” Int. J. Hydrogen Energy, Vol. 44, no. 51, pp. 27713–27725, Oct. 2019. DOI:10.1016/j.ijhydene.2019.09.011.
   Web of Science ®Google Scholar
• T. Demirdelen, F. Ekinci, B. D. Mert, İ Karasu, and M. Tümay, “Green touch for hydrogen production via alkaline electrolysis: The semi-flexible PV panels mounted wind turbine design, production and performance analysis,” Int. J. Hydrogen Energy, Vol. 45, no. 18, pp. 10680–10695, Apr. 2020. DOI:10.1016/j.ijhydene.2020.02.007.
   Web of Science ®Google Scholar
• G. Peharz, F. Dimroth, and U. Wittstadt, “Solar hydrogen production by water splitting with a conversion efficiency of 18%,” Int. J. Hydrogen Energy, Vol. 32, no. 15, pp. 3248–3252, Oct. 2007. DOI:10.1016/j.ijhydene.2007.04.036.
   Web of Science ®Google Scholar
• D. Martinez, and R. Zamora, “MATLAB simscape model of an alkaline electrolyser and its simulation with a directly coupled PV module,” Int. J. Renew. Energy Res., Vol. 8, no. 1, pp. 9, 2018.
   Web of Science ®Google Scholar
• R. B. Bollipo, S. Mikkili, and P. K. Bonthagorla, “Application of radial basis neural network in MPPT technique for stand-alone PV system under partial shading conditions,” IETE J. Res., 1–22, Oct. 2021. DOI:10.1080/03772063.2021.1988874.
   Web of Science ®Google Scholar
• S. N. Mughal, Y. R. Sood, and R. K. Jarial, “A neural network-based time-series model for predicting global solar radiations,” IETE J. Res., 1–13, Jun. 2021. DOI:10.1080/03772063.2021.1934576.
   Google Scholar
• P. M. Kumar, R. Saravanakumar, A. Karthick, and V. Mohanavel, “Artificial neural network-based output power prediction of grid-connected semitransparent photovoltaic system,” Environ. Sci. Pollut. Res., Vol. 29, no. 7, pp. 10173–10182, Feb. 2022. DOI:10.1007/s11356-021-16398-6.
   PubMed Web of Science ®Google Scholar
• North China Electric Power University, Y. K. Semero, J. Zhang, North China Electric Power University, D. Zheng, and Goldwind Science and Technology Co., Ltd., “PV power forecasting using an integrated GA-PSO-ANFIS approach and Gaussian process regression based feature selection strategy,” CSEE JPES, Vol. 4, no. 2, pp. 210–218, Jun. 2018. DOI:10.17775/CSEEJPES.2016.01920.
   Google Scholar
• F. Almonacid, P. J. Pérez-Higueras, E. F. Fernández, 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, Sep. 2014. DOI:10.1016/j.enconman.2014.05.090.
   Web of Science ®Google Scholar
• F. Touati, et al., “Long-term performance analysis and power prediction of PV technology in the State of Qatar,” Renewable Energy, Vol. 113, pp. 952–965, Dec. 2017. DOI:10.1016/j.renene.2017.06.078.
   Web of Science ®Google Scholar
• G. Notton, C. Voyant, A. Fouilloy, J. L. Duchaud, and M. L. Nivet, “Some applications of ANN to solar radiation estimation and forecasting for energy applications,” Appl. Sci., Vol. 9, no. 1, pp. 209, Jan. 2019. DOI:10.3390/app9010209.
   Google Scholar
• K. Gairaa, A. Khellaf, Y. Messlem, and F. Chellali, “Estimation of the daily global solar radiation based on Box–Jenkins and ANN models: A combined approach,” Renew. Sustain. Energy Rev., Vol. 57, pp. 238–249, May 2016. DOI:10.1016/j.rser.2015.12.111.
   Web of Science ®Google Scholar
• V. Z. Antonopoulos, D. M. Papamichail, V. G. Aschonitis, and A. V. Antonopoulos, “Solar radiation estimation methods using ANN and empirical models,” Comput. Electron. Agric., Vol. 160, pp. 160–167, May 2019. DOI:10.1016/j.compag.2019.03.022.
   Web of Science ®Google Scholar
• J. Alonso-Montesinos, et al., “The use of ANN and conventional solar-plant meteorological variables to estimate atmospheric horizontal extinction,” J. Clean. Prod., Vol. 285, pp. 125395, Feb. 2021. DOI:10.1016/j.jclepro.2020.125395.
   Web of Science ®Google Scholar
• C. Wan, J. Zhao, Y. Song, Z. Xu, J. Lin, and Z. Hu, “Photovoltaic and solar power forecasting for smart grid energy management,” CSEE J. Power Energy Syst., Vol. 1, no. 4, pp. 38–46, Dec. 2015. DOI:10.17775/CSEEJPES.2015.00046.
   Google Scholar
• W. Lee, K. Kim, J. Park, J. Kim, and Y. Kim, “Forecasting solar power using long-short term memory and convolutional neural networks,” IEEE Access., Vol. 6, pp. 73068–73080, 2018. DOI:10.1109/ACCESS.2018.2883330.
   Web of Science ®Google Scholar
• H. Sharadga, S. Hajimirza, and R. S. Balog, “Time series forecasting of solar power generation for large-scale photovoltaic plants,” Renew. Energy, Vol. 150, pp. 797–807, May 2020. DOI:10.1016/j.renene.2019.12.131.
   Web of Science ®Google Scholar
• M. Pierro, et al., “Data-driven upscaling methods for regional photovoltaic power estimation and forecast using satellite and numerical weather prediction data,” Sol. Energy, Vol. 158, pp. 1026–1038, Dec. 2017. DOI:10.1016/j.solener.2017.09.068.renene.2019.12.131.
   Web of Science ®Google Scholar
• A. Koca, H. F. Oztop, Y. Varol, and G. O. Koca, “Estimation of solar radiation using artificial neural networks with different input parameters for Mediterranean region of Anatolia in Turkey,” Expert. Syst. Appl., Vol. 38, no. 7, pp. 8756–8762, 2011. DOI:10.1016/j.eswa.2011.01.085.
   Web of Science ®Google Scholar
• M. M. Rahman, et al., “Prospective methodologies in hybrid renewable energy systems for energy prediction using artificial neural networks,” Sustainability, Vol. 13, no. 4, pp. 2393, Feb. 2021. DOI:10.3390/su13042393.
   Web of Science ®Google Scholar
• M. Abdel-Nasser, and K. Mahmoud, “Accurate photovoltaic power forecasting models using deep LSTM-RNN,” Neural Comput. Appl., Vol. 31, no. 7, pp. 2727–2740, Jul. 2019. DOI:10.1007/s00521-017-3225-z.
   Web of Science ®Google Scholar
• C.-J. Huang, and P.-H. Kuo, “Multiple-input deep convolutional neural network model for short-term photovoltaic power forecasting,” IEEE Access., Vol. 7, pp. 74822–74834, 2019. DOI:10.1109/ACCESS.2019.2921238.
   Web of Science ®Google Scholar
• S. Leva, A. Dolara, F. Grimaccia, M. Mussetta, and E. Ogliari, “Analysis and validation of 24 hours ahead neural network forecasting of photovoltaic output power,” Math. Comput. Simul., Vol. 131, pp. 88–100, Jan. 2017. DOI:10.1016/j.matcom.2015.05.010.
   Web of Science ®Google Scholar
• S. A. Haider, M. Sajid, and S. Iqbal, “Forecasting hydrogen production potential in Islamabad from solar energy using water electrolysis,” Int. J. Hydrogen Energy, Vol. 46, no. 2, pp. 1671–1681, Jan. 2021. DOI:10.1016/j.ijhydene.2020.10.059.
   Web of Science ®Google Scholar
• V. H. Quej, J. Almorox, J. A. Arnaldo, and L. Saito, “ANFIS, SVM and ANN soft-computing techniques to estimate daily global solar radiation in a warm sub-humid environment,” J. Atmos. Sol. Terr. Phys., Vol. 155, pp. 62–70, Mar. 2017. DOI:10.1016/j.jastp.2017.02.002.
   Web of Science ®Google Scholar
• L. Visser, T. AlSkaif, and W. van Sark, “Operational day-ahead solar power forecasting for aggregated PV systems with a varying spatial distribution,” Renew. Energy, Vol. 183, pp. 267–282, Jan. 2022. DOI:10.1016/j.renene.2021.10.102.
   Web of Science ®Google Scholar
• M. Talaat, T. Said, M. A. Essa, and A. Y. Hatata, “Integrated MFFNN-MVO approach for PV solar power forecasting considering thermal effects and environmental conditions,” Int. J. Electr. Power Energy Syst., Vol. 135, pp. 107570, Feb. 2022. DOI:10.1016/j.ijepes.2021.107570.
   Web of Science ®Google Scholar
• B. Shboul, et al., “A new ANN model for hourly solar radiation and wind speed prediction: A case study over the north & south of the Arabian Peninsula,” Sustain. Energy Technol. Assess., Vol. 46, pp. 101248, Aug. 2021. DOI:10.1016/j.seta.2021.101248.
   Web of Science ®Google Scholar
• S. Yolcular, “Hydrogen production for energy use in European union countries and Turkey,” Energy Sourc. Part A Recov. Utiliz. Environ. Eff., Vol. 31, no. 15, pp. 1329–1337, Aug. 2009. DOI:10.1080/15567030802089615.
   Web of Science ®Google Scholar
• D. E. Rumelhart, G. E. Hinton, and R. J. Williams. Learning internal representations by error propagation. California Univ San Diego La Jolla Inst for Cognitive Science, San Diego, 1985.
   Google Scholar
• C. A. Coello Coello, and R. L. Becerra, “Efficient evolutionary optimization through the use of a cultural algorithm,” Eng. Optim., Vol. 36, no. 2, pp. 219–236, 2004.
   Web of Science ®Google Scholar
• C.-H. Je, and H. M. Kim, “A study on the water splitting using polymer electrolyte membrane for producing hydrogen and oxygen,” Int. J. Electrochem. Sci., 6948–6975, Jul. 2019. DOI:10.20964/2019.07.64.
   Web of Science ®Google Scholar
• M. Gardiner. DOE hydrogen and fuel cells program record# 9013: energy requirements for hydrogen gas compression and liquefaction as related to vehicle storage needs. Washington, DC: US Department of Energy, 2009.
   Google Scholar