Site Suitability Analysis for the Establishment of Wind Energy Farm in Delta State Using Geospatial Techniques

The rising demand for electricity in Nigeria continues to outpace supply, with over 40% of the population lacking reliable access to grid-based power. The overdependence on fossil fuels has further strained the national energy system, while contributing to environmental degradation and greenhouse gas emissions. Renewable energy, particularly wind power, has been widely recognized as a viable alternative to diversify the energy mix, improve rural electrification, and promote environmental sustainability. However, the development of wind energy infrastructure in Nigeria remains limited, and its spatial distribution is heavily skewed toward inland and northern regions such as Sokoto, Jos, and Katsina, where wind speeds are comparatively higher. The study aimed at a site suitability analysis for wind energy farms in Delta State, Nigeria using geospatial technology. The objectives are to: establish the criteria and factors for locating wind energy farms in Delta State; classify the criteria and factors according to their rank of suitability for locating wind energy farms in Delta State; apply weighted linear combination of the classified criteria and factors to determine suitable sites for wind energy farms in Delta state and produce a suitability index map showing areas suitable for locating wind energy farms in Delta state. The study employed a geospatial multi-criteria evaluation (MCE) framework integrated with the Analytical Hierarchy Process (AHP) to assess windfarm site suitability. Seven influencing factors were analyzed: wind power density (WPD), elevation, slope, soil type, proximity to road networks, proximity to waterbodies, and land use/land cover (LULC). Each parameter was derived from remotely sensed data, thematic maps, and secondary geospatial datasets, then standardized and reclassified into suitability levels. Expert judgment was used to construct a pairwise comparison matrix, and factor weights were derived through AHP with a Consistency Ratio (CR) of 0.03, indicating reliable judgments. A weighted overlay analysis was applied to generate a windfarm suitability index map, while sensitivity analysis tested the stability of the model under ±20% weight variations of key factors. The suitability analysis revealed that wind power density, elevation, and slope collectively accounted for more than 53% of the total model weight, establishing them as the primary drivers of wind energy potential. Proximity to road networks and soil type moderately influenced feasibility, while hydrology and LULC exerted localized but secondary control. The final suitability map showed that 5.11% (834.41 km²) of the study area was highly suitable, 62.29% (10,180.46 km²) was moderately suitable, and 32.60% (5,328.39 km²) was low suitability. Highly suitable areas were concentrated in the northern and northeastern uplands, where strong and consistent winds (>100 W/m²), moderate elevation, stable soils, and good road access converged. Sensitivity analysis confirmed the robustness of the model, as moderate changes in factor weights produced only minor adjustments in suitability extents without altering the overall ranking of influencing criteria. It is recommended that the AHP-based multi-criteria evaluation framework used in this study be adopted by state energy agencies and private investors as a decision-support tool for future wind energy projects in Delta State.