A hybrid improved GA-SVM framework for multi-zonal wind speed prediction: Case of Cameroon’s diverse climatic regimes

Accurate wind speed estimation is required for integrating wind energy into Cameroon’s hydropower-dependent national grid. However, Cameroon’s diverse climatic zones present significant challenges for modelling. This study develops and evaluates a Hybrid Improved Genetic Algorithm-Support Vector Machine (IGA-SVM) framework for wind speed prediction across five climatic regions, with subsequent conversion to wind power density (WPD). Using 40 years of NASA’ Prediction of Worldwide Energy Resources (POWER) meteorological data (temperature, relative humidity, atmospheric pressure, rainfall, wind direction, snowfall, wind speed, and snow depth), the Hybrid IGA-SVM eliminates manual hyperparameter tuning by optimizing the SVM’s box constraint (C), kernel scale (γ), and epsilon-insensitivity (ε), independently for each zone. The model achieves R 2 values from 0.7956 to 0.8634 across all terrains. The Adamawa Plateau shows the highest stability and predictability, while the Sudano-Sahelian zone shows the greatest wind power density despite seasonal volatility. However, none of the zones reached the National Renewable Energy Laboratory (NREL) Class 3 thresholds (300–400 W/m 2 ); the highest wind power density (WPD) (81 W/m 2 in the Western Highlands) falls into Class 2, indicating suitability for small to medium scale or hybrid applications. The proposed model can serve as a preliminary screening tool for regional energy planning in Cameroon. All recommendations require on-site validation and economic analysis (e.g., levelized cost of energy (LCOE)) before investment decisions. Importantly, all zone-specific recommendations in this paper are based exclusively on technical metrics (R 2 , Root Mean Square Error (RMSE), wind speed, and wind power density); no economic analysis (levelized cost of energy, net present value, or payback period) were performed, and thus these recommendations should be interpreted as pre-feasibility guidance for prioritizing zones for further study, and not as final investment advice.