Modelling Nonseasonal Daily Clearness Index for Solar Energy Estimation in Ilorin, Nigeria Using Support Vector Regression


  • Nsikan Ime Obot University of Lagos, Akoka, Lagos, Nigeria
  • Okwisilieze Uwadoka University of Lagos, Akoka, Lagos, Nigeria
  • Oluwasegun Israel Ayayi University of Lagos, Akoka, Lagos, Nigeria


Clearness index, support vector regression, global solar radiation, radial basic function, kernel function


Communication in Physical Sciences, 2024, 11(2): 233-247

Authors: Nsikan Ime Obot*, Okwisilieze Uwadoka, and Oluwasegun Israel Ayayi

Received: 23 August 2023/Accepted: 17 April 2024

Solar radiation is the primary energy source for the planet and is crucial for energy generation in technologies such as photovoltaic systems and solar thermal food dryers. However, accurately quantifying solar radiation poses challenges due to its variability and the lack of appropriate instrumentation, among other factors. To address this, support vector regression (SVR), a machine learning (ML) algorithm, was employed using various kernel functions such as linear, radial basis function (RBF), and sigmoid, with hyperparameter tuning. This approach aimed to estimate the daily clearness index ( ), which is a key metric for estimating global solar radiation at Ilorin (8° 32′ N, 4° 34′ E), Nigeria. The SVR models were developed and assessed by considering statistical measures such as the correlation coefficient and mean absolute error. The input parameters used in the model included sunshine hours, maximum temperature, minimum temperature, and the ratio of both temperatures. The correlations between  and its estimators, and between its actual and calculated values were all below 70%. SVR-RBF outperformed the others, including the traditional regression model, under the statistical assessment measures, both with the training dataset and the testing dataset. Although the regression model obtained under the same conditions surpassed the other kernel functions in some areas and is highly competitive, SVR-RBF is recommended for the estimation of the daily clearness index in this vicinity.



Download data is not yet available.

Author Biographies

Nsikan Ime Obot, University of Lagos, Akoka, Lagos, Nigeria

Physics Department, Faculty of Science

Okwisilieze Uwadoka, University of Lagos, Akoka, Lagos, Nigeria

Mechanical Engineering Department, Faculty of Engineering

Oluwasegun Israel Ayayi, University of Lagos, Akoka, Lagos, Nigeria

Physics Department, Faculty of Science


Al‐Waeli, A.H., Kazem, H.A., Chaichan, M.T., &Sopian, K. (2021). A review of photovoltaic thermal systems: Achievements and applications. International Journal of Energy Research, 45(2), pp. 1269–1308.

Angstrom, A. (1924). Solar and terrestrial radiation. Quarterly Journal of the Royal Meteorological Society 50, pp. 121–125.

Ayodele, T.R., Ogunjuyigbe, A.S.O., Amedu, A. & Munda, J.L. (2019). Prediction of global solar irradiation using hybridized k-means and support vector regression algorithms. Renewable Energy Focus , 29, pp. 78–93.

Babatunde, E.B. & Aro, T.O. (1995). Relationship between "clearness index" and "cloudiness index" at a tropical station (Ilorin, Nigeria). Renewable Energy 6, pp. 801–805.

Paulescu, M., Paulescu, E., Gravila, P., Badescu, V., Paulescu, M., Paulescu, E., Gravila, P. & Badescu, V. (2013). Weather modeling and forecasting of PV systems operation, Vol. 358, London, Springer.

Basak, D., Pal, S. & Patranabis, D.C. (2007). Support vector regression. Neural Information Processing-Letters and Reviews, 11, pp. 203–224.

Belaid, S. & Mellit, A. (2016). Prediction of daily and mean monthly global solar radiation using support vector machine in an arid climate. Energy Conversion and Management 118, pp. 105–118.

Fonseca Jr., J.G.S., Oozeki, T., Takashima, T., Koshimizu, G., Uchida, Y. & Ogimoto, K. (2011). Use of support vector regression and numerically predicted cloudiness to forecast power output of a photovoltaic power plant in Kitakyushu, Japan. Progress in Photovoltaics: Research and Applications, 20, pp. 874–882.

Besharat, F., Dehghan, A.A. & Faghih, A.R. (2013). Empirical models for estimating global solar radiation: a review and case study. Renewable and Sustainable Energy Reviews, 21, pp. 798–821.

Chukwujindu, N.S. (2017). A comprehensive review of empirical models for estimating global solar radiation in Africa. Renewable and Sustainable Energy Reviews, 78, pp. 955–995.

Nwokolo, S. C. & Ogbulezie, J.C. (2018). A quantitative review and classification of empirical models for predicting global solar radiation in West Africa. Beni-Suef University Journal of Basic and Applied Sciences , 7, pp. 367–396.

Smola, A. &Schölkopf, B. (2004). A tutorial on support vector regression. Statistics and Computing 14, pp. 199–222.

Eltbaakh, Y.A., Ruslan, M.H., Alghoul, M.A., Othman, M.Y., Sopian, K. and Razykov, T.M. (2012). Solar attenuation by aerosols: an overview. Renewable and Sustainable Energy Reviews 16, pp. 4264–4276.

Grojean, R.E., Sousa, J.A. & Henry, M.C. (1980). Utilization of solar radiation by polar animals: an optical model for pelts. I 19, pp. 339–348.

Martins, G.S. & Giesbrecht, M. (2021). Clearness index forecasting: a comparative study between a stochastic realization method and a machine learning algorithm. Renewable Energy 180, pp. 787–805.

Ramli, M.A.M., Twaha, S. & Al-Turki, Y.A. (2015). Investigating the performance of support vector machine and artificial neural networks in predicting solar radiation on a tilted surface: Saudi Arabia case study. Energy Conversion and Management 105, pp. 442–452.

Singh, V. (2024). The environment and its components. In: Textbook of Environment and Ecology, Singapore, Springer, pp. 1–13.

Mohanty, S., Patra, P. K. & Sahoo, S. S. (2016). Prediction and application of solar radiation with soft computing over traditional and conventional approach – a comprehensive review. Renewable and Sustainable Energy Reviews, 56, pp. 778–796.

Obot, N.I., Akanbi, S.A., Ajiboye, A.A. & Chendo, M.A.C. (2022). Charcoal and gravel basin lined solar still for brackish water purification. Journal of Current Science and Technology, 12, pp. 110–127. doi: 10.14456/jcst.2022.11.

Obot, N.I., Olubgon, B., Humphrey, I. & Akeem, R.A. (2023). Equatorial all-sky downward longwave radiation modelling. Communication in Physical Sciences 9(2), pp. 111–124.

Olayinka, S (2011). Estimation of global and diffuse solar radiations for selected cities in Nigeria. International Journal of Energy and Environment Engineering 3, pp. 13–33.

Okoye, C.O., Taylan, O. & Baker, D.K. (2016). Solar energy potentials in strategically located cities in Nigeria: review, resource assessment and PV system design. Renewable and Sustainable Energy Reviews 55, pp. 550–566.

Udo, S. O. (2000). Sky conditions at Ilorin as characterized by clearness index and relative sunshine. Solar Energy 69, pp. 45–53.

Udo, S.O. & Aro, T.O. (1999). Measurement of global, solar global photosynthetically active and downward infrared radiations at Ilorin, Nigeria. Renewable Energy, 06, pp. 113–122.

Vapnik, V. & Chervonenkis, A.Y. (1964). A class of algorithms for pattern recognition learning. Avtomat. i Telemekh, 25, pp. 937–945.

Zendehboudi, A., Baseer, M.A. and Saidur, R. (2018). Application of support vector machine models for forecasting solar and wind energy resources: a review. Journal of Cleaner Production 199, pp. 272–285.

Hinrichsen, K. (1994). The Ångström formula with coefficients having a physical meaning. Solar Energy 52, pp. 491–495.