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Empirical correlations as a means for estimating monthly average daily global radiation: A critical overview

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  • Halawa, Edward
  • GhaffarianHoseini, AmirHosein
  • Hin Wa Li, Danny

Abstract

In regions where solar energy is abundant, solar energy can play a vital role in attaining energy sustainability. Sizing solar energy systems requires the availability of solar radiation data on horizontal surface which can then be used to calculate solar radiation intensity on any tilted surface using appropriate conversion factors or formula. In many parts of the world, especially in developing countries, such data is not readily available. Many researchers have found that monthly average daily value of global solar radiation on horizontal surface can be estimated when meteorological parameters such as duration of sunshine, number of rainy days, relative humidity, etc. are available. Many empirical correlations have been developed based on this approach. The development of such a correlation has been made possible through the availability of solar and other meteorological data required for their validation. This paper presents a review on the existing empirical correlations and critically looks at the practicality of such correlations. This raises the question on the appropriateness of the past and present approaches adopted by researchers in this field. The paper also discusses various related aspects and proposes new directions for future research.

Suggested Citation

  • Halawa, Edward & GhaffarianHoseini, AmirHosein & Hin Wa Li, Danny, 2014. "Empirical correlations as a means for estimating monthly average daily global radiation: A critical overview," Renewable Energy, Elsevier, vol. 72(C), pages 149-153.
    • Handle: RePEc:eee:renene:v:72:y:2014:i:c:p:149-153
    DOI: 10.1016/j.renene.2014.07.004
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    Cited by:

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    2. Halabi, Laith M. & Mekhilef, Saad & Hossain, Monowar, 2018. "Performance evaluation of hybrid adaptive neuro-fuzzy inference system models for predicting monthly global solar radiation," Applied Energy, Elsevier, vol. 213(C), pages 247-261.
    3. Polo, J. & Gastón, M. & Vindel, J.M. & Pagola, I., 2015. "Spatial variability and clustering of global solar irradiation in Vietnam from sunshine duration measurements," Renewable and Sustainable Energy Reviews, Elsevier, vol. 42(C), pages 1326-1334.
    4. Mohamed Chaibi & EL Mahjoub Benghoulam & Lhoussaine Tarik & Mohamed Berrada & Abdellah El Hmaidi, 2021. "An Interpretable Machine Learning Model for Daily Global Solar Radiation Prediction," Energies, MDPI, vol. 14(21), pages 1-19, November.
    5. Olubayo M. Babatunde & Josiah L. Munda & Yskandar Hamam, 2020. "Exploring the Potentials of Artificial Neural Network Trained with Differential Evolution for Estimating Global Solar Radiation," Energies, MDPI, vol. 13(10), pages 1-18, May.
    6. Li, Danny H.W. & Lou, Siwei, 2018. "Review of solar irradiance and daylight illuminance modeling and sky classification," Renewable Energy, Elsevier, vol. 126(C), pages 445-453.
    7. Lanre Olatomiwa & Saad Mekhilef & Shahaboddin Shamshirband & Dalibor Petkovic, 2015. "RETRACTED ARTICLE: Potential of support vector regression for solar radiation prediction in Nigeria," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 77(2), pages 1055-1068, June.
    8. Olatomiwa, Lanre & Mekhilef, Saad & Shamshirband, Shahaboddin & Petković, Dalibor, 2015. "Adaptive neuro-fuzzy approach for solar radiation prediction in Nigeria," Renewable and Sustainable Energy Reviews, Elsevier, vol. 51(C), pages 1784-1791.

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