Gis-Based Spatial Classification of Onshore Wind Energy Potential in Nigeria
Authors
Department of Process Engineering and Energy Technology, Hochschule Bremerhaven, Bremerhaven (Germany)
Department of Process Engineering and Energy Technology, Hochschule Bremerhaven, Bremerhaven (Germany)
Department of Medical Biometry/Biostatistics, University of Bremen, Bremen (Germany)
Department of Environmental Physics, University of Bremen, Bremen (Germany)
Department of Process Engineering and Energy Technology, Hochschule Bremerhaven, Bremerhaven (Germany)
Sam Kem Farms LTD, Kano (Nigeria)
Department of Process Engineering and Energy Technology, Hochschule Bremerhaven, Bremerhaven (Germany)
Department of Mechanical Engineering/Mechatronics, The Federal Polytechnic, Ilaro (Nigeria)
Article Information
DOI: 10.51584/IJRIAS.2026.110200049
Subject Category: Renewable energy
Volume/Issue: 11/2 | Page No: 535-547
Publication Timeline
Submitted: 2026-02-12
Accepted: 2026-02-18
Published: 2026-03-07
Abstract
Nigeria’s electricity sector continues to experience chronic shortages despite abundant natural resources. This study assesses the spatial potential of onshore wind energy across Nigeria through integrated Geographic Information Systems (GIS) and Multi-Criteria Decision Analysis (MCDA). Hourly wind data from NASA POWER and ERA5 reanalysis were extrapolated to 150 m hub height and combined with topographic, land cover, and socio-environmental datasets to evaluate national wind suitability. The analysis reveals a pronounced north–south gradient in wind resources, with the northern regions exhibiting higher mean wind speeds and more favourable topography for large-scale wind power development. Using the Analytical Hierarchy Process (AHP), suitability was classified into three categories (Most Preferred, Preferred, and Least Preferred) based on wind potential, land-use conflicts, and population pressure. The Most Preferred zone, covering approximately 18 % of Nigeria’s land area, lies predominantly in the northern savanna belt, offering the greatest opportunity for utility-scale deployment. The resulting spatial classification provides a strategic framework for targeted wind energy investment and forms a foundation for subsequent techno-economic and policy assessments.
Keywords
Analytical Hierarchy Process, GIS, MCDA, Nigeria
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References
1. Akpaneno, A. F., & Idris, A. U. (2024). Assessment of Wind Energy Resources and its Energy Potential in three states of Northwest Nigeria. Asian Journal of Research and Reviews, 7(1), 12-20. doi:https://doi.org/10.9734/ajr2p/2024/v7i1179 [Google Scholar] [Crossref]
2. Ayodele, T. R., Ogunjuyigbe, A. S., Munda, J. L., & Odigie, O. (2012). Wind power utilization assessment and site matching in Nigeria using GIS. Renewable Energy, 37(1), 3273-2281. doi:https://doi.org/10.1016/j.renene.2012.06.049 [Google Scholar] [Crossref]
3. Dalero, M. S., & Musa, N. A. (2018). A brief review on Assessment of Wind Energy Resources in Nigeria. Solar and Wind Technology , 37(1), 45-52. doi:https://doi.org/10.1016/j.renene.2012.06.049 [Google Scholar] [Crossref]
4. Ember. (2025). Carbon Intensity of Electricity Generation, 2000 to 2024. New York: Statisticsl Review of World Review. doi:https://ember-climate.org/data/ [Google Scholar] [Crossref]
5. Ember. (n.d.). Carbon intensity of electricity gereration, 2000 to 2024. [Google Scholar] [Crossref]
6. Fadare, D. A. (2008). A statistical Analysis of Wind Energy Potential in Ibadan, Nigeria, based on the Weibull Distribution Function. Pacific Journal of Science and Technology, 9(1), 110-119. [Google Scholar] [Crossref]
7. Gualtieri, G. (2019). A comprehensive review on wind resource extrapolation models applied in wind energy. Renewable and Sustainable Energy Reviews, 102, 215-233. doi:https://doi.org/10.1016/j.rser.2018.12.002 [Google Scholar] [Crossref]
8. Lu, X., Mc Elroy, M. B., & Kivilvona, J. (2009). Global Potential for Wind Generating Energy. Sustainable Science, 106(27), 10933-10938. doi:https://doi.org/10.1073/pnas.0904101106 [Google Scholar] [Crossref]
9. Mas'ud, A. A., Wirba, A., Ardila-Rey, A., Sanchez , M. F., Sukki, M. F., Duque, A. J., . . . Munir, A. B. (2017). Wind Power Potentials in Cameron and Nigeria: Lessons from South Africa. Energies, 100-120. [Google Scholar] [Crossref]
10. NERC. (2023). Renewable Energy Mix of Nigeria. Abuja: Nigerian Electricity Regulatory Commission. [Google Scholar] [Crossref]
11. Nigeria Bureau of Statistics. (2024). Nigerian Population. Abuja: Nigeria Bureau of Statistics. [Google Scholar] [Crossref]
12. NPEEEP. (2015). Energy Transition Plan. Abuja: National Renewable and Energy Efficiency Policy. [Google Scholar] [Crossref]
13. Olangunju, O. (2020). Assessment of the viability of wind farm projects in Northern Nigeria (Master's Thesis). Hamburg: Hamburg University of Applied Sciences. [Google Scholar] [Crossref]
14. Omotayo-Tomo, M. S., & Onukwube, O. (2022). Statistical Assessment of the Wind Energy Potential of South East Nigeria using the Weibull Model. Coast Journal of the School of Science, 5-8. [Google Scholar] [Crossref]
15. United Nations Framework Convention. (2022). Climate Change (COP 27). Paris: UNFCCC. [Google Scholar] [Crossref]
16. World Bank. (2023). Access to Electricity (Nigeria in context). Chicago: World Develoment Indicators. Retrieved from https://data.worldbank.org/indicator/EG.ELC.ACCS.ZS?locations=NG [Google Scholar] [Crossref]
17. World Bank. (2025). Share of Nigerian population without electricity. New York: Our World in Data. [Google Scholar] [Crossref]
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