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Gis-Based Spatial Classification of Onshore Wind Energy Potential in Nigeria

Author

Listed:
  • Adewunmi Andrew Adesanmi

    (Department of Process Engineering and Energy Technology, Hochschule Bremerhaven, Bremerhaven)

  • Ajayi Oluwajuwon Emmanuel

    (Department of Process Engineering and Energy Technology, Hochschule Bremerhaven, Bremerhaven)

  • Mannir Abdu

    (Department of Medical Biometry/Biostatistics, University of Bremen, Bremen)

  • Achigbulam Charles

    (Department of Environmental Physics, University of Bremen, Bremen)

  • Sani Khadijat

    (Department of Process Engineering and Energy Technology, Hochschule Bremerhaven, Bremerhaven)

  • Biliaminu Samuel Akeem

    (Sam Kem Farms LTD, Kano)

  • Shubham Singh

    (Department of Process Engineering and Energy Technology, Hochschule Bremerhaven, Bremerhaven)

  • Olalekan Awolola

    (Department of Mechanical Engineering/Mechatronics, The Federal Polytechnic, Ilaro)

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.

Suggested Citation

  • Adewunmi Andrew Adesanmi & Ajayi Oluwajuwon Emmanuel & Mannir Abdu & Achigbulam Charles & Sani Khadijat & Biliaminu Samuel Akeem & Shubham Singh & Olalekan Awolola, 2026. "Gis-Based Spatial Classification of Onshore Wind Energy Potential in Nigeria," International Journal of Research and Innovation in Applied Science, International Journal of Research and Innovation in Applied Science (IJRIAS), vol. 11(2), pages 535-547, February.
  • Handle: RePEc:bjf:journl:v:11:y:2026:i:2:p:535-547
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    References listed on IDEAS

    as
    1. Abdullahi Abubakar Mas’ud & Asan Vernyuy Wirba & Jorge Alfredo Ardila-Rey & Ricardo Albarracín & Firdaus Muhammad-Sukki & Álvaro Jaramillo Duque & Nurul Aini Bani & Abu Bakar Munir, 2017. "Wind Power Potentials in Cameroon and Nigeria: Lessons from South Africa," Energies, MDPI, vol. 10(4), pages 1-19, March.
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