Statistical Modelling of Solar Energy Potential in Abuja and Nasarawa State, Nigeria

Authors

  • Terkimbi Tor Department of Physics, University of Abuja, Abuja, Nigeria
  • George Gala Nyam Department of Physics, University of Abuja, Abuja, Nigeria
  • Damilola Oluwafemi Samson Department of Physics, University of Abuja, Abuja, Nigeria | Department of Diagnostic Imaging and Radiotherapy, National University of Malaysia, Malaysia
  • Jonathan Asake Department of Physics, University of Abuja, Abuja, Nigeria
  • Abba Babakura Department of Physics, University of Abuja, Abuja, Nigeria

DOI:

https://doi.org/10.63561/jmns.v2i3.867

Keywords:

Solar Radiation, Descriptive Statistics, Nimet, Federal Capital Territory, Nasarawa State

Abstract

This study presents a predictive modeling approach to evaluate the solar energy potential in Nigeria’s Federal Capital Territory (FCT), Abuja, and Nasarawa State over a ten-year period using statistical methods. The research integrates descriptive statistics and solar radiation modeling to assess both the electricity generation potential and atmospheric clarity of the two regions. Results show that both FCT and Nasarawa consistently experience average daily solar radiation levels exceeding 17.5 MJ/m²/day, which surpasses the global threshold for high solar energy potential. Specifically, solar radiation in FCT ranges from 11.4 to 22.0 MJ/m²/day, with an annual total of 211.7 MJ/m²/day, while Nasarawa State records 11.8 to 22.2 MJ/m²/day, totaling 209.6 MJ/m²/day. Reliability indices during off-rainy seasons are 0.62 for FCT and 0.56 for Nasarawa, indicating favorable conditions for solar power generation. A photovoltaic (PV) performance analysis using a 550W panel predicts annual energy outputs of 6.206 × 10⁷ MWh for FCT and 2.27 × 10⁸ MWh for Nasarawa. The clearness index (Kt) varies from 0.414 to 0.665 in FCT and 0.432 to 0.662 in Nasarawa, with slightly clearer atmospheric conditions observed in FCT. Skewness values of –0.379 (FCT) and –0.220 (Nasarawa) suggest left-tailed distributions, while kurtosis values of 1.322 and 1.168 indicate platykurtic behaviour. These findings validate the use of statistical models in predicting solar energy potential and highlight the suitability of both regions for long-term solar power deployment.

References

Adavuruku, B. M., Aghaegbunam, E. J., Chidozie, I. K., & Stephen, M. A. (2022). Geospatial analysis of solar energy potentials in Niger State, Nigeria. American Journal of Modern Physics, 11(6), 85–94. https://www.sciencepg.com/article/10.11648/j.ajmp.20221106.12

Adisa, R. A., & Yusuf, A. M. (2025). Solar power for rural electrification: A modeling perspective. Energy for Sustainable Development, 74, 1–10. https://doi.org/10.1016/j.esd.2023.102452

Akinyele, D. O., Balogun, A. O., & Lawal, A. I. (2023). Integrating energy storage with solar power systems in Nigeria. Energy Policy, 174, 113389. https://doi.org/10.1016/j.enpol.2023.113389

Al Garni, H. Z., & Awasthi, A. (2021). A comparative study of AI techniques for solar potential modeling. Energy Reports, 7, 319–330. https://doi.org/10.1016/j.egyr.2021.07.072

Ayodele, T. R., Adekitan, A. I., & Okonkwo, E. C. (2025). Scaling solar deployment in West Africa: Challenges and strategies. Renewable Energy, 206, 315–328. https://doi.org/10.1016/j.renene.2023.11.081

Bekele, G., Shiferaw, A., & Tilahun, M. (2020). GIS-based solar energy potential assessment in Ethiopia. Renewable Energy Focus, 34, 12–20. https://doi.org/10.1016/j.ref.2020.07.001

Bello, A., & Salami, A. T. (2024). Geospatial modeling of solar potential in northern Nigeria. Energy Reports, 10, 1320–1332. https://doi.org/10.1016/j.egyr.2023.12.043

Chanchangi, Y. N., Ghosh, A., Sundararajan, A., & Mallick, T. K. (2023). Assessment of solar radiation as a power generation source in Nigeria. Journal of Renewable and Sustainable Energy, 15(1), 013102. https://doi.org/10.1063/5.0133696

Chidiebere, A., Nwachukwu, C. M., & Eze, J. C. (2025). Urban solar potential mapping using LiDAR and GIS. Journal of Cleaner Production, 390, 140872. https://doi.org/10.1016/j.jclepro.2024.140872

Dada, J. A., Ogunmodimu, O., & Eleri, E. O. (2024). Policy support for solar energy investment in Nigeria. Sustainable Energy Technologies and Assessments, 56, 103187. https://doi.org/10.1016/j.seta.2022.103187

Hassan, A. M., Usman, A. M., & Musa, Y. A. (2019). Prediction of solar radiation using machine learning techniques. Renewable and Sustainable Energy Reviews, 101, 289–307. https://doi.org/10.1016/j.rser.2018.11.002

Ikejemba, E. C. X., Schuur, P. C., & Van Hillegersberg, J. (2021). Sustainable energy development in West Africa: Solar potential and barriers. Renewable and Sustainable Energy Reviews, 140, 110750. https://doi.org/10.1016/j.rser.2021.110750

Johnson, O. B., Taiwo, A. E., & Mohammed, S. (2025). AI-driven solar energy forecasting models: Review and future directions. Applied Energy, 342, 121009. https://doi.org/10.1016/j.apenergy.2023.121009

Kumar, R., Singh, A., & Yadav, A. (2021). Review of recent modeling techniques for solar power prediction. Energy Conversion and Management, 230, 113818. https://doi.org/10.1016/j.enconman.2020.113818

Mekonnen, Y., & Mahajan, R. (2020). Solar energy modeling in East Africa. Renewable Energy, 162, 1892–1905. https://doi.org/10.1016/j.renene.2020.09.062

Mezghani, A., Trabelsi, A., & Ouarda, T. B. M. J. (2020). Climate change impacts on solar radiation availability. Environmental Modelling & Software, 127, 104697. https://doi.org/10.1016/j.envsoft.2020.104697

Nwokolo, N. C., & Nwachukwu, C. M. (2023). Modeling renewable energy access in Nigeria. Renewable Energy Focus, 46, 31–42. https://doi.org/10.1016/j.ref.2023.03.004

Ogunmodimu, O., & Okoroigwe, E. C. (2019). Modeling solar energy potential in Nigeria. Energy Reports, 5, 104–113. https://doi.org/10.1016/j.egyr.2019.07.003

Okonkwo, E. C., Aja, O. C., & Eze, J. C. (2024). Hybrid modeling of solar power generation in tropical climates. Heliyon, 10, e17111. https://doi.org/10.1016/j.heliyon.2024.e17111

Olatomiwa, L., Mekhilef, S., & Sanusi, K. O. (2023). Renewable energy integration for sustainable electricity supply in Nigeria: A review. Renewable and Sustainable Energy Reviews, 180, 113980. https://doi.org/10.1016/j.rser.2023.113980

Onuoha, K. M., Uzochukwu, S. C., & Okafor, A. O. (2025). Future trends in solar modeling research in sub-Saharan Africa. Renewable and Sustainable Energy Reviews, 181, 114748. https://doi.org/10.1016/j.rser.2023.114748

Sunda, D., & Alam, M. (2020). A review on solar radiation modeling techniques. Solar Energy, 202, 350–367. https://doi.org/10.1016/j.solener.2020.02.048

Tanimu, M., Suleiman, R., & Ameen, A. (2025). Climate-smart solar energy development in Nigeria. Energy Strategy Reviews, 47, 101029. https://doi.org/10.1016/j.esr.2023.101029

Teka, A., Gebre, T., & Eshetu, Y. (2021). Assessment of solar energy potential in sub-Saharan Africa. Journal of Cleaner Production, 291, 125907. https://doi.org/10.1016/j.jclepro.2021.125907

Tufa, R. A., Bekele, D., & Debela, D. (2022). Solar radiation forecasting using hybrid models. Applied Energy, 312, 118688. https://doi.org/10.1016/j.apenergy.2022.118688

Umeh, I. A., & Uchegbu, S. N. (2024). Assessment of long-term solar resources under climate variability. Journal of Environmental Management, 352, 120141. https://doi.org/10.1016/j.jenvman.2023.120141

Zhai, Z., Wang, L., & Liu, H. (2024). A remote sensing-based approach to solar farm site selection. Remote Sensing of Environment, 295, 113646. https://doi.org/10.1016/j.rse.2023.113646

Zhang, Y., Li, J., & Zhou, X. (2023). Assessment of global solar energy potential using machine learning. Nature Energy, 8, 343–354. https://doi.org/10.1038/s41560-023-01245-z.

Downloads

Published

2025-05-30

How to Cite

Tor, T., Nyam, G. G., Samson, D. O., Asake, J., & Babakura, A. (2025). Statistical Modelling of Solar Energy Potential in Abuja and Nasarawa State, Nigeria. Faculty of Natural and Applied Sciences Journal of Mathematical Modeling and Numerical Simulation, 2(3), 83–95. https://doi.org/10.63561/jmns.v2i3.867