Year: 2025
Type: article
Abstract: Accurate prediction of wind energy output from site-specific wind velocity data is essential for evaluating the feasibility and energy yield of wind farm installations. This study presents a predictive framework for wind power assessment based on daily wind velocity datasets from eight Iraqi cities: Duhok, Mosul, Kirkuk, Baghdad, Najaf, Wasit, Qadisiyyah, and Basra. The proposed method employs polynomial regression (POR) as a nonlinear estimation technique to correlate daily wind speed data with turbine power output. POR is selected for its computational simplicity and adequacy in capturing the nonlinearity inherent in wind power curves. The predictive models are calibrated individually for each city using historical wind data and the manufacturer's specified power curve for the turbine. The results indicate that the POR-based predictions closely align with the actual turbine power curve across all sites, demonstrating low prediction error and strong curve-fitting behavior. Notably, Wasit, Qadisiyyah, and Basra exhibited the highest potential for wind energy generation, with annual predicted outputs exceeding 2000 kWh per turbine, indicating promising conditions for small-scale wind energy exploitation. In contrast, northern cities such as Duhok and Mosul yielded significantly lower outputs (<1000 kWh annually), suggesting limited economic viability under the studied configuration.
Author: Hussein Saleh
Language: English
Subfield: Aerospace Engineering
Field: Engineering
Domain: Physical Sciences
Open Access status: diamond
OpenAlex ID: W4414837770