IDEAS home Printed from https://ideas.repec.org/a/bjc/journl/v13y2026i2p367-375.html

Artificial Neural Network-Based Modeling for Monthly Average Global Solar Radiation Estimation in South East Nigeria

Author

Listed:
  • Valentine S. Enyi

    (Department of Electrical and Electronic Engineering, State University of Medical and Applied Sciences (SUMAS), Igbo-Eno Enugu State)

  • Nkeriuka P. Okozor

    (Department of Computer Engineering, State University of Medical and Applied Sciences (SUMAS), Igbo-Eno Enugu)

  • Raphael C. Eze

    (Department of Electrical and Electronic Engineering, State University of Medical and Applied Sciences (SUMAS), Igbo-Eno Enugu State)

Abstract

Reliable estimation of global solar radiation (GSR) is essential for the proper planning and performance evaluation of solar power systems. This research presents a neural network–based approach for estimating monthly mean GSR across South East Nigeria, covering Enugu, Imo, Ebonyi, Anambra, and Abia States. Twenty years of meteorological records (2005–2025), including air temperature, relative humidity, and wind speed, were utilized for model development. A feedforward multilayer perceptron trained using the backpropagation technique was implemented for the prediction task. Model evaluation indicates good agreement between predicted and observed values, with a mean absolute percentage error (MAPE) of less than 5%, a coefficient of determination (R²) of 0.95451, and a root mean square error (RMSE) of 0.32 MJ/m²/day. The findings demonstrate that the developed model can effectively estimate solar radiation in tropical locations where measured solar data are scarce. This approach can support informed decision-making in the design and expansion of solar energy projects within the region.

Suggested Citation

  • Valentine S. Enyi & Nkeriuka P. Okozor & Raphael C. Eze, 2026. "Artificial Neural Network-Based Modeling for Monthly Average Global Solar Radiation Estimation in South East Nigeria," International Journal of Research and Scientific Innovation, International Journal of Research and Scientific Innovation (IJRSI), vol. 13(2), pages 367-375, February.
    • Handle: RePEc:bjc:journl:v:13:y:2026:i:2:p:367-375
    as

    Download full text from publisher

    File URL: https://rsisinternational.org/journals/ijrsi/uploads/vol13-iss2-pg367-375-202602_pdf.pdf
    Download Restriction: no
    File URL: https://rsisinternational.org/journals/ijrsi/view/artificial-neural-network-based-modeling-for-monthly-average-global-solar-radiation-estimation-in-south-east-nigeria/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Yacef, R. & Benghanem, M. & Mellit, A., 2012. "Prediction of daily global solar irradiation data using Bayesian neural network: A comparative study," Renewable Energy, Elsevier, vol. 48(C), pages 146-154.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Rohani, Abbas & Taki, Morteza & Abdollahpour, Masoumeh, 2018. "A novel soft computing model (Gaussian process regression with K-fold cross validation) for daily and monthly solar radiation forecasting (Part: I)," Renewable Energy, Elsevier, vol. 115(C), pages 411-422.
    2. Shubham Gupta & Amit Kumar Singh & Sachin Mishra & Pradeep Vishnuram & Nagaraju Dharavat & Narayanamoorthi Rajamanickam & Ch. Naga Sai Kalyan & Kareem M. AboRas & Naveen Kumar Sharma & Mohit Bajaj, 2023. "Estimation of Solar Radiation with Consideration of Terrestrial Losses at a Selected Location—A Review," Sustainability, MDPI, vol. 15(13), pages 1-29, June.
    3. Yusen Wang & Wenlong Liao & Yuqing Chang, 2018. "Gated Recurrent Unit Network-Based Short-Term Photovoltaic Forecasting," Energies, MDPI, vol. 11(8), pages 1-14, August.
    4. Seung Chan Jo & Young Gyu Jin & Yong Tae Yoon & Ho Chan Kim, 2021. "Methods for Integrating Extraterrestrial Radiation into Neural Network Models for Day-Ahead PV Generation Forecasting," Energies, MDPI, vol. 14(9), pages 1-18, May.
    5. Majid Almaraashi, 2017. "Short-term prediction of solar energy in Saudi Arabia using automated-design fuzzy logic systems," PLOS ONE, Public Library of Science, vol. 12(8), pages 1-16, August.
    6. Linares-Rodriguez, Alvaro & Ruiz-Arias, José Antonio & Pozo-Vazquez, David & Tovar-Pescador, Joaquin, 2013. "An artificial neural network ensemble model for estimating global solar radiation from Meteosat satellite images," Energy, Elsevier, vol. 61(C), pages 636-645.
    7. 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.
    8. Olivencia Polo, Fernando A. & Ferrero Bermejo, Jesús & Gómez Fernández, Juan F. & Crespo Márquez, Adolfo, 2015. "Failure mode prediction and energy forecasting of PV plants to assist dynamic maintenance tasks by ANN based models," Renewable Energy, Elsevier, vol. 81(C), pages 227-238.
    9. Salcedo-Sanz, Sancho & Deo, Ravinesh C. & Cornejo-Bueno, Laura & Camacho-Gómez, Carlos & Ghimire, Sujan, 2018. "An efficient neuro-evolutionary hybrid modelling mechanism for the estimation of daily global solar radiation in the Sunshine State of Australia," Applied Energy, Elsevier, vol. 209(C), pages 79-94.
    10. Fan, Junliang & Wu, Lifeng & Zhang, Fucang & Cai, Huanjie & Zeng, Wenzhi & Wang, Xiukang & Zou, Haiyang, 2019. "Empirical and machine learning models for predicting daily global solar radiation from sunshine duration: A review and case study in China," Renewable and Sustainable Energy Reviews, Elsevier, vol. 100(C), pages 186-212.
    11. Pang, Zhihong & Niu, Fuxin & O’Neill, Zheng, 2020. "Solar radiation prediction using recurrent neural network and artificial neural network: A case study with comparisons," Renewable Energy, Elsevier, vol. 156(C), pages 279-289.
    12. Massi Pavan, A. & Mellit, A. & De Pieri, D. & Kalogirou, S.A., 2013. "A comparison between BNN and regression polynomial methods for the evaluation of the effect of soiling in large scale photovoltaic plants," Applied Energy, Elsevier, vol. 108(C), pages 392-401.
    13. Cecilia Martinez-Castillo & Gonzalo Astray & Juan Carlos Mejuto, 2021. "Modelling and Prediction of Monthly Global Irradiation Using Different Prediction Models," Energies, MDPI, vol. 14(8), pages 1-16, April.
    14. Teke, Ahmet & Yıldırım, H. Başak & Çelik, Özgür, 2015. "Evaluation and performance comparison of different models for the estimation of solar radiation," Renewable and Sustainable Energy Reviews, Elsevier, vol. 50(C), pages 1097-1107.
    15. Prasad, Ramendra & Ali, Mumtaz & Kwan, Paul & Khan, Huma, 2019. "Designing a multi-stage multivariate empirical mode decomposition coupled with ant colony optimization and random forest model to forecast monthly solar radiation," Applied Energy, Elsevier, vol. 236(C), pages 778-792.
    16. Anamika, & Peesapati, Rajagopal & Kumar, Niranjan, 2016. "Estimation of GSR to ascertain solar electricity cost in context of deregulated electricity markets," Renewable Energy, Elsevier, vol. 87(P1), pages 353-363.
    17. Kambezidis, H.D. & Psiloglou, B.E. & Karagiannis, D. & Dumka, U.C. & Kaskaoutis, D.G., 2017. "Meteorological Radiation Model (MRM v6.1): Improvements in diffuse radiation estimates and a new approach for implementation of cloud products," Renewable and Sustainable Energy Reviews, Elsevier, vol. 74(C), pages 616-637.
    18. Mohanty, Sthitapragyan & Patra, Prashanta Kumar & Sahoo, Sudhansu Sekhar, 2016. "Prediction and application of solar radiation with soft computing over traditional and conventional approach – A comprehensive review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 56(C), pages 778-796.
    19. Deo, Ravinesh C. & Wen, Xiaohu & Qi, Feng, 2016. "A wavelet-coupled support vector machine model for forecasting global incident solar radiation using limited meteorological dataset," Applied Energy, Elsevier, vol. 168(C), pages 568-593.
    20. Rui Han & Shiqi Yang, 2023. "A Study on Industrial Heritage Renewal Strategy Based on Hybrid Bayesian Network," Sustainability, MDPI, vol. 15(13), pages 1-32, July.

    More about this item

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:bjc:journl:v:13:y:2026:i:2:p:367-375. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Dr. Renu Malsaria (email available below). General contact details of provider: https://rsisinternational.org/journals/ijrsi/ .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.