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Metaheuristic-Based Predictive Modelling of Wind Velocity Distributions

Ekeoma, Chukwuma George, Nwaogu Chibuzo Jackie, Esenamunjor, Clement, Temidayo

Abstract

Accurate modeling of wind velocity distribution is essential for optimizing wind energy generation and improving resource assessments. This study presents a predictive stochastic model utilizing the Weibull distribution, with its parameters optimized using the Particle Swarm Optimization (PSO) algorithm—a robust metaheuristic technique. Wind speed data collected over a one-year period in the Aba region were analyzed. The PSO-based estimation significantly outperformed traditional methods like Maximum Likelihood Estimation (MLE), yielding lower error margins and higher correlation with empirical data. The optimized model achieved a high coefficient of determination (R² = 0.972) and a reduced Root Mean Square Error (RMSE = 0.0087), confirming its effectiveness. The study demonstrates the potential of PSO-enhanced models in supporting reliable wind energy resource evaluation and system design.

Keywords

Wind Speed ModelingWeibull DistributionParticle Swarm OptimizationMetaheuristicsRenewable EnergyStochastic Modeling

References

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