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

Authors

Adewunmi Andrew Adesanmi

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

Ajayi Oluwajuwon Emmanuel

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

Mannir Abdu

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

Achigbulam Charles

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

Sani Khadijat

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

Biliaminu Samuel Akeem

Sam Kem Farms LTD, Kano (Nigeria)

Shubham Singh

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

Olalekan Awolola

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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