assessing wind energy potential in markazi province, ‎iran: a data-driven approach with ai algorithms

This paper investigates the wind energy potential in markazi province, iran, focusing on three ‎cities: tafresh, khomein, and saveh. the primary objective of this study is to provide a ‎comprehensive analysis of wind patterns using a combination of statistical approaches and ‎artificial intelligence techniques. wind data was collected from advanced meteorological ‎stations in these cities over two years (2018–2020), including detailed measurements of ‎wind speed and direction at 10-minute intervals. this high-resolution dataset facilitated an in-depth examination of wind behavior and its suitability for energy production. statistical ‎analysis was conducted using the weibull distribution and wind rose diagrams, which provided ‎insights into the wind characteristics and their spatial variations. additionally, long short-term memory (lstm) networks were employed to predict wind speeds and temporal trends. ‎these models effectively captured the complex relationships within the wind data and produced ‎highly accurate forecasts. the comparison of actual and predicted wind rose diagrams ‎demonstrated a strong alignment, validating the reliability of the lstm-based predictions in ‎reflecting real-world wind patterns. the results of this study demonstrate that combining ‎traditional statistical methods with modern machine learning techniques provides a robust ‎framework for analyzing wind energy potential. by leveraging these tools, the study offers ‎valuable insights for sustainable energy planning and supports informed decision-making for ‎renewable energy investments in markazi province‎.‎