article · Wind Engineering
Integrating wind power into Cameroon's hydropower-dependent grid requires dependable wind speed forecasts across diverse climatic zones. A hybrid framework combining an improved genetic algorithm with support vector machines models wind speeds and calculates wind power density across five distinct regions. Using forty years of meteorological data, the automated approach independently tunes model parameters for each terrain, achieving correlation values between 0.7956 and 0.8634. The Adamawa Plateau demonstrates the highest predictability and stability, whereas the Sudano-Sahelian zone exhibits the largest wind power density alongside seasonal volatility. None of the investigated zones achieve National Renewable Energy Laboratory Class 3 thresholds. The peak wind power density of 81 watts per square metre in the Western Highlands falls into Class 2, showing suitability for small to medium scale or hybrid systems rather than large installations.
Accurate wind resource assessment helps nations diversify electricity grids currently reliant on hydropower. By mapping wind speeds and power potential across complex climatic terrains, regional planners can identify viable sites for renewable generation. These insights demonstrate that while Cameroon lacks utility-scale wind resources, targeted areas could support hybrid or medium-scale renewable energy systems to strengthen local energy resilience.
The framework functions as an early-stage screening tool for energy planners, utilities, and project developers assessing renewable additions to Cameroon's grid. It remains at a pre-feasibility stage, intended to prioritise prospective sites rather than guide final investments. Before commercial deployment, findings require on-site validation using physical measurements alongside detailed economic evaluations, such as levelised cost of energy and payback analyses, tailored to small or medium scale installations.
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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.
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DOI: 10.1177/0309524x261480850
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