An Artificial Neural Network Model for Estimating Daily Solar Radiation in Northwest Nigeria

Salisu Aliyu, Aminu S Zakari, Muhammad Ismail, Mohammed A Ahmed


Solar energy has attracted enormous attention as it plays an essential role in meeting the ever growing sustainable and environmental friendly energy demand of the world. Due to the high cost of calibration and maintenance of designated measuring instruments, solar radiation data are limited not only in Nigeria but in most parts of the world. The optimal design of solar energy systems requires accurate estimation of solar radiation. Existing studies are focused on the analysis of monthly or annual solar radiation with less attention paid to the determination of daily solar radiation. Estimating daily solar radiation envisages high quality and performance of solar systems. In this paper, an Artificial Neural Network data mining model is proposed for estimating the daily solar radiation in Kano, Kaduna and Katsina, North West Nigeria. Daily Solar radiation data for 21years collected from the Nigerian Metrological Agency were used as training/testing data while developing the model. Two statistical indicators: coefficient of determination (R2) and the root mean square error (RMSE) were used to evaluate the model. An RMSE of 0.47 and 0.48 was obtained for the training and testing dataset respectively, while an R2 of 0.78 was obtained for both the training and testing dataset. The overall results showed that artificial neural network exhibits excellent performance for the estimation of daily solar radiation.

Keywords— Artificial Neural Network, Data mining, Solar Radiation 

Full Text:



Aliyu, S., Musa, H., & Jauro, F. (2018). Performance comparison of two Decision tree algorithms based on splitting criteria for predicting child birth delivery type. Paper presented at the 1st International Conference on Education and Development, Baze University, Abuja-Nigeria.

Bou-Rabee, M., Sulaiman, S. A., Saleh, M. S., & Marafi, S. (2017). Using artificial neural networks to estimate solar radiation in Kuwait. Renewable and Sustainable Energy Reviews, 72, 434-438.

Eludoyin, O. M. (2011). Air Temperature and Relative Humidity Areal Distribution over Nigeria. Ife Research Publications in Geography, 10(1), 134-145.

Inayat, A., & Raza, M. (2019). District cooling system via renewable energy sources: A review. Renewable and Sustainable Energy Reviews, 107, 360-373.

Jiang, H., Dong, Y., Wang, J., & Li, Y. (2015). Intelligent optimization models based on hard-ridge penalty and RBF for forecasting global solar radiation. Energy Conversion and Management, 95, 42-58.

Kazem, H. A., Yousif, J. H., & Chaichan, M. T. (2016). Modeling of daily solar energy system prediction using support vector machine for Oman. International Journal of Applied Engineering Research, 11(20), 10166-10172.

Mohandes, M. A. (2012). Modeling global solar radiation using Particle Swarm Optimization (PSO). Solar Energy, 86(11), 3137-3145.

Naveen, C. (2018). Analysis of different combinations of meteorological parameters in predicting the horizontal global solar radiation with ANN approach: A case study. Renewable and Sustainable Energy Reviews, 91, 248-258.

Notton, G., Voyant, C., Fouilloy, A., Duchaud, J. L., & Nivet, M. L. (2019). Some applications of ANN to solar radiation estimation and forecasting for energy applications. Applied Sciences, 9(1), 209.

Olatomiwa, L., Mekhilef, S., Shamshirband, S., & Petković, D. (2015). Adaptive neuro-fuzzy approach for solar radiation prediction in Nigeria. Renewable and Sustainable Energy Reviews, 51, 1784-1791.

Ozgoren, M., Bilgili, M., & Sahin, B. (2012). Estimation of global solar radiation using ANN over Turkey. Expert systems with applications, 39(5), 5043-5051.

Qin, J., Chen, Z., Yang, K., Liang, S., & Tang, W. (2011). Estimation of monthly-mean daily global solar radiation based on MODIS and TRMM products. Applied energy, 88(7), 2480-2489.

Şenkal, O. (2010). Modeling of solar radiation using remote sensing and artificial neural network in Turkey. Energy, 35(12), 4795-4801.

Şenkal, O., & Kuleli, T. (2009). Estimation of solar radiation over Turkey using artificial neural network and satellite data. Applied energy, 86(7-8), 1222-1228.

Udo, R. K. (1970). Geographical Regions of Nigeria.: University of California Press.

Yadav, A. K., Malik, H., & Chandel, S. (2015). Application of rapid miner in ANN based prediction of solar radiation for assessment of solar energy resource potential of 76 sites in Northwestern India. Renewable and Sustainable Energy Reviews, 52, 1093-1106



  • There are currently no refbacks.

Copyright (c) 2020 The Author(s)

Creative Commons License
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.

Powered by ICT and Faculty of Engineering, FUOYE

Copyright © 2021 The Author(s). Published by Faculty of Engineering, FUOYE

image The FUOYEJET website and her metadata are licensed under CC BY-NC