Assessment of wind energy potential in northwestern Iraq: A hybrid optimization method based on Weibull distribution and intelligent algorithms

This study presents a comprehensive comparative analysis of conventional and artificial intelligence (AI)-based methods for estimating Weibull distribution parameters using 10 years (2013-2023) of hourly wind speed data from six sites in northwestern Iraq: AlKaam, AlShirgat, Baiji, Nenwa, Sinjar, and Haweja. Three traditional methods, the method of moments (MOM), the maximum likelihood method (MLM), and the least squares method (LSM), are compared against three AI algorithms: particle swarm optimization (PSO), differential evolution (DE), and genetic algorithm (GA). Model performance is evaluated using statistical indicators, including RMSE, MAE, R2, and chi2. Results show that PSO consistently outperforms all other methods, achieving the highest coefficient of determination (R2) of 0.998 and the lowest error values across all locations. Among conventional techniques, MLM achieved superior accuracy, with an average RMSE of 0.073 and an R2 of 0.57. Wind resource assessment revealed Haweja as the most promising site, with a mean wind speed of 5.69 m s–1, followed by AlKaam (5.59 m s–1) and Baiji (5.57 m s–1). Economic evaluation showed that using PSO-estimated parameters significantly improves energy forecasting accuracy and reduces the levelized cost of energy (LCOE) to as low as $0.03/kWh at Haweja. The 2.5 MW WT5 turbine achieved optimal performance, generating over 8000 MWh year–1 with capacity factors exceeding 39% at high-potential sites. This work provides a robust framework for accurate wind energy potential assessment in Iraq and similar arid regions, demonstrating the critical advantage of AI-driven parameter estimation in enhancing both technical and economic feasibility of wind power projects.