An Empirical Approach to Solar Photovoltaic Cell Temperature Prediction

[1] Ding Q., Huang J., Chen J., Luo X. Climate warming, renewable energy consumption and rare earth market: Evidence from the United States. Energy 2024:290:130276. https://doi.org/10.1016/j.energy.2024.130276 Search in Google Scholar

[2] Blumberga D., Chen B., Ozarska A., Indzere Z. Lauka D. Energy, Bioeconomy, Climate Changes and Environment Nexus. Environmental and Climate Technologies 2019:23(3):370–392. https://doi.org/10.2478/rtuect-2019-0102 Search in Google Scholar

[3] Kostevica V., Dzikevics M. Bibliometric Analysis of the Climate Change Impact on Energy Systems. Environmental and Climate Technologies 2023:27(1):950–963. https://doi.org/10.2478/rtuect-2023-0069 Search in Google Scholar

[4] Khamisani A. A., Liu D. P. P., Cloward D. J., Bai D. R. Design Methodology of Off-Grid PV Solar Powered System (A Case Study of Solar Powered Bus Shelter). Search in Google Scholar

[5] Liu Q., Yu G., Liu J. J. Solar Radiation as Large-Scale Resource for Energy-Short World. Energy Environ. 2009:20(3):319–329. https://doi.org/10.1260/095830509788066466 Search in Google Scholar

[6] Iheanetu K. J. Solar Photovoltaic Power Forecasting: A Review. Sustainability 2022:14(24):17005. https://doi.org/10.3390/su142417005 Search in Google Scholar

[7] Bodnár I., Matusz-Kalász D., Koós D. Experimental and numerical analysis of solar cell temperature transients. Pollack Periodica 2021:16(2):104–109. https://doi.org/10.1556/606.2020.00260 Search in Google Scholar

[8] Shaik F., Lingala S. S., Veeraboina P. Effect of various parameters on the performance of solar PV power plant: a review and the experimental study. Sustainable Energy Research 2023:10(1):6. https://doi.org/10.1186/s40807-023-00076-x Search in Google Scholar

[9] Jathar L. D., et al. Comprehensive review of environmental factors influencing the performance of photovoltaic panels: Concern over emissions at various phases throughout the lifecycle. Environ. Pollution 2023:326:121474. https://doi.org/10.1016/j.envpol.2023.121474 Search in Google Scholar

[10] Ceylan İ., Erkaymaz O., Gedik E., Gürel A. E. The prediction of photovoltaic module temperature with artificial neural networks. Case Stud. Therm. Eng. 2014:3:11–20. https://doi.org/10.1016/j.csite.2014.02.001 Search in Google Scholar

[11] Schiro F., Benato A., Stoppato A., Destro N. Improving photovoltaics efficiency by water cooling: Modelling and experimental approach. Energy 2017:137:798–810. https://doi.org/10.1016/j.energy.2017.04.164 Search in Google Scholar

[12] Ansari E., Akhtar M. N., Othman W. A. F. W., Abu Bakar E., Alhady S. S. N. Numerical Investigation of Thermal Efficiency of a Solar Cell. Applied Sciences 2022:12(21):10887. https://doi.org/10.3390/app122110887 Search in Google Scholar

[13] Parthiban R., Ponnambalam P. An Enhancement of the Solar Panel Efficiency: A Comprehensive Review. Front. Energy Res. 2022:10. https://doi.org/10.3389/fenrg.2022.937155 Search in Google Scholar

[14] Cheraghizade M., Jamali-Sheini F. Photovoltaic behavior of SnS solar cells under temperature variations. Optik 2022:254:168635. https://doi.org/10.1016/j.ijleo.2022.168635 Search in Google Scholar

[15] Wei Z. et al. Understanding the temperature sensitivity of the photovoltaic parameters of perovskite solar cells. Solar Energy 2023:264:112040. https://doi.org/10.1016/j.solener.2023.112040 Search in Google Scholar

[16] Piotrowski L. J., Simões M. G., Farret F. A. Feasibility of water-cooled photovoltaic panels under the efficiency and durability aspects. Solar Energy 2020:207:103–109. https://doi.org/10.1016/j.solener.2020.06.087 Search in Google Scholar

[17] Kersten F. et al. Degradation of multicrystalline silicon solar cells and modules after illumination at elevated temperature. Solar Energy Materials and Solar Cells 2015:142:83–86. https://doi.org/10.1016/j.solmat.2015.06.015 Search in Google Scholar

[18] Taghinia A., Yazdi F., Fazel P., Anousheh S. N., Davoudi K. G. Comparison of single junction GaAs and In0.2Ga0.8N based solar cells at various temperatures. Energy Procedia 2012:14:919–924. https://doi.org/10.1016/j.egypro.2011.12.1033 Search in Google Scholar

[19] Liao W., Heo Y., Xu S. Evaluation of Temperature Dependent Models for PV Yield Prediction. [Online]. [Accessed 18.09.2021]. Available: https://www.semanticscholar.org/paper/Evaluation-of-Temperature-Dependent-Models-for-PV-Liao-Heo/a232d5d270cfdbef9feef9e603e64ba3c314d59d Search in Google Scholar

[20] Kamuyu W. C. L., J. Won L. C., Ahn H. Prediction Model of Photovoltaic Module Temperature for Power Performance of Floating PVs. Energies 2018:11(2):447. https://doi.org/10.3390/en11020447 Search in Google Scholar

[21] Du Y., Tao W., Liu Y., Jiang J., Huang H. Heat transfer modeling and temperature experiments of crystalline silicon photovoltaic modules. Solar Energy 2017:146:257–263. https://doi.org/10.1016/j.solener.2017.02.049 Search in Google Scholar

[22] Vijaykumar R., Rudramoorthy R., Mangalore A. R. Prediction of Solar PV Panel Temperature Using Mathematical Models and Artificial Neural Networks. J. Comput. Theor. Nanosci. 2017:14(10):4986–4997. https://doi.org/10.1166/jctn.2017.6909 Search in Google Scholar

[23] Coskun C., Koçyiğit N., Oktay Z. Estimation of pv module surface temperature using artificial neural networks. Mugla J. Sci. Technol. 2016:2(2). https://doi.org/10.22531/muglajsci.283611 Search in Google Scholar

[24] Motuzienė V., Bielskus J., Lapinskienė V., Rynkun G. Office Building’s Occupancy Prediction Using Extreme Learning Machine Model with Different Optimization Algorithms. Environ. Clim. Technol. 2021:25(1):525–536. https://doi.org/10.2478/rtuect-2021-0038 Search in Google Scholar

[25] Serrano-Luján L., Toledo C., Colmenar J. M., Abad J., Urbina A. Accurate thermal prediction model for buildingintegrated photovoltaics systems using guided artificial intelligence algorithms. Applied Energy 2022:315:119015. https://doi.org/10.1016/j.apenergy.2022.119015 Search in Google Scholar

[26] Jošt M. et al. Perovskite Solar Cells go Outdoors: Field Testing and Temperature Effects on Energy Yield. Adv. Energy Mater. 2020:10(25):2000454. https://doi.org/10.1002/aenm.202000454 Search in Google Scholar

[27] Meng Q. et al. Effect of temperature on the performance of perovskite solar cells. J. Mater. Sci. Mater. Electron. 2020:32:12784–12792. https://doi.org/10.1007/s10854-020-03029-y Search in Google Scholar

[28] Khaledi P., Behboodnia M., Karimi M. Simulation and Optimization of Temperature Effect in Solar Cells CdTe with Back Connection Cu2O. Int. J. Opt. 2022:e1207082. https://doi.org/10.1155/2022/1207082 Search in Google Scholar

[29] Zhang C., Zhang Y., Su J., Gu T., Yang M. Performance prediction of PV modules based on artificial neural network and explicit analytical model. J. Renew. Sustain. Energy 2020:12(1):013501. https://doi.org/10.1063/1.5131432 Search in Google Scholar

[30] Paulescu M. et al. Online Forecasting of the Solar Energy Production. Ann. West Univ. Timisoara – Phys. 2018:60(1):104–110. https://doi.org/10.2478/awutp-2018-0011 Search in Google Scholar

[31] Mishra R., Tiwari G. Energy and exergy analysis of hybrid photovoltaic thermal water collector for constant collection temperature mode. Solar Energy 2013:90:58–67. https://doi.org/10.1016/j.solener.2012.12.022 Search in Google Scholar

[32] Dubey S., Solanki S. C., Tiwari A. Energy and exergy analysis of PV/T air collectors connected in series. Energy Build. 2009:41. https://doi.org/10.1016/j.enbuild.2009.03.010 Search in Google Scholar

[33] Fawagreh K., Gaber M. M., Elyan E. Random forests: from early developments to recent advancements. Syst. Sci. Control Eng. 2014:2(1):602–609. https://doi.org/10.1080/21642583.2014.956265 Search in Google Scholar

[34] Breiman L. Random Forests. Mach. Learn. 2001:45(1):5–32. https://doi.org/10.1023/A:1010933404324 Search in Google Scholar

[35] Schonlau M., Zou R. Y. The random forest algorithm for statistical learning. Stata J. Promot. Commun. Stat. Stata 2020:20(1):3–29. https://doi.org/10.1177/1536867X20909688 Search in Google Scholar

[36] Amiry H. et al. Assessment of improved models for predicting PV module temperature and their electrical performance in a semi-arid coastal region. Int. J. Green Energy 2023:20(14):1584–1596. https://doi.org/10.1080/15435075.2023.2166788 Search in Google Scholar

[37] Gholami A. et al. Impact of harsh weather conditions on solar photovoltaic cell temperature: Experimental analysis and thermal-optical modeling. Solar Energy 2023:252:176–194. https://doi.org/10.1016/j.solener.2023.01.039 Search in Google Scholar

[38] Du Y. et al. Evaluation of photovoltaic panel temperature in realistic scenarios. Energy Convers. Manag. 2016:108:60–67. https://doi.org/10.1016/j.enconman.2015.10.065 Search in Google Scholar