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.‎