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Implicit meteorological parameter-based empirical models for estimating back temperature solar modules under varying tilt-angles in Lagos, Nigeria

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  • Obiwulu, Anthony Umunnakwe
  • Chendo, Michael A.C.
  • Erusiafe, Nald
  • Nwokolo, Samuel Chukwujindu

Abstract

In this study, measured back temperature of solar modules tilted at angles 6.7°, 16.8°, 26.8° and 0° respectively were used to develop requisite models for estimating this entity for solar modules used in Lagos (Latitude 6.6080oN and Longitude 3.6218oE) Nigeria. Overall solar radiation received by the solar modules showed an appreciable energy gain of 8.74%, 20.85% and 19.49% respectively over that generated by the horizontally placed module. A thorough statistical performance analysis of the obtained data yielded twelve models for each of the solar modules, a total of forty-eight models in all. These models were tested to know which that gave the most accurate regression model for estimating the back temperature in the environment. Application of statistical indicators such as MBE, MPE, RMSE, RRMSE, R2 and GPI showed that model 8 for the module tilted at 16.8° registered the most performance followed by model 19 for module tilted at 6.7°, model 28 for module tilted at 26.8° and lastly, model 44 for module oriented at 0°. Similar analysis carried out on the established models for the four modules in comparison with existing models, showed that model 44 performed excellently in terms of accuracy compared to other models.

Suggested Citation

  • Obiwulu, Anthony Umunnakwe & Chendo, Michael A.C. & Erusiafe, Nald & Nwokolo, Samuel Chukwujindu, 2020. "Implicit meteorological parameter-based empirical models for estimating back temperature solar modules under varying tilt-angles in Lagos, Nigeria," Renewable Energy, Elsevier, vol. 145(C), pages 442-457.
    • Handle: RePEc:eee:renene:v:145:y:2020:i:c:p:442-457
    DOI: 10.1016/j.renene.2019.05.136
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    Citations

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    Cited by:

    1. Rehman, Tauseef-ur & Qaisrani, Mumtaz A. & Shafiq, M. Basit & Baba, Yousra Filali & Aslfattahi, Navid & Shahsavar, Amin & Cheema, Taqi Ahmad & Park, Cheol Woo, 2025. "Global perspectives on advancing photovoltaic system performance—A state-of-the-art review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 207(C).
    2. Obiwulu, Anthony Umunnakwe & Erusiafe, Nald & Olopade, Muteeu Abayomi & Nwokolo, Samuel Chukwujindu, 2020. "Modeling and optimization of back temperature models of mono-crystalline silicon modules with special focus on the effect of meteorological and geographical parameters on PV performance," Renewable Energy, Elsevier, vol. 154(C), pages 404-431.
    3. Fangyi Xu & Jihong Wang, 2025. "Harnessing Hybridized Machine Learning Algorithms for Sustainable Smart Production: A Case Study of Solar PV Energy in China," Journal of the Knowledge Economy, Springer;Portland International Center for Management of Engineering and Technology (PICMET), vol. 16(1), pages 3214-3264, March.
    4. Bailek, Nadjem & Bouchouicha, Kada & Hassan, Muhammed A. & Slimani, Abdeldjalil & Jamil, Basharat, 2020. "Implicit regression-based correlations to predict the back temperature of PV modules in the arid region of south Algeria," Renewable Energy, Elsevier, vol. 156(C), pages 57-67.
    5. Bayrak, Fatih, 2025. "Prediction of photovoltaic panel cell temperatures: Application of empirical and machine learning models," Energy, Elsevier, vol. 323(C).
    6. Kristina Kilikevičienė & Jonas Matijošius & Artūras Kilikevičius & Mindaugas Jurevičius & Vytautas Makarskas & Jacek Caban & Andrzej Marczuk, 2019. "Research of the Energy Losses of Photovoltaic (PV) Modules after Hail Simulation Using a Newly-Created Testbed," Energies, MDPI, vol. 12(23), pages 1-14, November.

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