Journal of Green Energy Research and Innovation
Quarterly

Assessing Wind Energy Potential in Markazi Province, ‎Iran: A Data-Driven Approach with AI Algorithms

Document Type : Research article

Authors

Amir Hossein Karamali 1
Abolghasem Daeichian 2
Saber Rezaei 1
Ali Reihanian 3

1 Department of Electrical Engineering, Iran University of Science and Technology, Tehran, Iran.

2 Department of Electrical Engineering, Faculty of Engineering, Arak University, Arak, Iran.

3 Department of Computer Engineering, Faculty of Engineering, Arak University, Arak, Iran.

https://doi.org/10.61186/jgeri.2.2.26
Abstract
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.‎

Graphical Abstract

Assessing Wind Energy Potential in Markazi Province, ‎Iran: A Data-Driven Approach with AI Algorithms

Highlights


❖ Suggesting suitable locations for wind power plants in Markazi Province by analyzing real wind speed data
❖ Analyzing wind patterns using a combination of statistical approaches and artificial intelligence techniques
❖ Conducting statistical analysis using the Weibull distribution and wind rose diagrams
❖ Employing Long Short-Term Memory network to predict wind speeds and temporal trends.

Keywords

Wind Energy Potential
Wind rose
Weibull Function
Long Short-Term Memory (LSTM)
Renewable energy

Declaration of Competing Interest

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper. The ethical issues, including plagiarism, informed consent, misconduct, data fabrication and/or falsification, double publication and/or submission, redundancy, have been completely observed by the authors.

Credit Authorship Contribution Statement
Amir Hossein Karamali: Data curation, Formal analysis, Software, Validation, Visualization, Roles/Writing-original draft. Abolghasem Daeichian: Conceptualization, Methodology, Project administration, Supervision, Writing-review & editing. ‎ Saber Rezaei: Data curation, Formal analysis, Software, Validation, Visualization, Roles/Writing-original draft. Ali Reihanian: Methodology, Supervision, Writing-review & editing.

Bibliography

Amir Hossein Karamali was born in 2000 in Mahallat, Iran. He completed his bachelor's degree in ‎Electrical Engineering at Arak University in 2022, and is currently pursuing a master's degree in ‎Power Electronics and Electric Machines at Iran University of Science and Technology (IUST). His ‎research focuses on power electronics and the application of artificial intelligence in power systems, ‎aiming to contribute to the development of advanced technologies for a sustainable future.

Abolghasem Daeichian was born in Iran, in 1982. He received the Bachelor's degree in electrical ‎engineering from the Isfahan University of Technology in 2003, and the M.Sc. and Ph.D. degree in ‎control engineering from Shiraz University and Isfahan University of Technology in 2006 and 2014, ‎respectively. He visited Australian National University in 2012. Since 2017, he has been with the ‎Department of Electrical Engineering, Faculty of Engineering, Arak University, where he is currently ‎an Associate Professor. He was the head of Research Institute of Renewable Energy, Arak University, ‎Arak, Iran from 2021 to 2023 and the IT manager at Arak University from 2023 to 2024. His research ‎interests include estimation & filtering, stochastic control, quantum control, and cyber-physical ‎systems. Dr. Daeichian is a fellow of the Iranian Society of Instrumentation and Control Engineers.‎

Saber Rezaei was born in 1996 in Tehran, Iran. He completed his bachelor's degree in Electrical Engineering with a focus on Control Engineering at Semnan University in 2020. Currently, he is pursuing a Master's degree in Neurocognitive Engineering at Iran University of Science and Technology (IUST). His expertise lies in signal processing, artificial intelligence and machine learning, with a particular interest in deep learning and its applications in biomedical engineering.‎

Ali Reihanian received his B.Sc. degree in Information Technology (IT) from Mazandaran University of ‎Science and Technology, Iran, in 2011. He received his M.Sc. degree in Information Technology (IT), ‎with special focus on Artificial Intelligence, from Mazandaran University of Science and Technology, ‎Iran, in 2014. He also received his Ph.D. degree in Artificial Intelligence and Robotics from University ‎of Tabriz, Iran, in 2018. Since 2021, he has been with the Department of Computer Engineering, ‎Faculty of Engineering, Arak University, where he is currently an Assistant Professor. Also, he has ‎been the head of Research Institute of Artificial Intelligence, Arak University, Arak, Iran, since 2023. ‎His research interests include machine learning and pattern recognition, social network analysis, natural ‎language processing and data mining‎.‎

Citation
A. H. Karamali, A. Daeichian, S. Rezaei, and A. Reihanian," Assessing Wind Energy Potential in Markazi Province, Iran: A Data-Driven Approach with AI Algorithms," Journal of Green Energy Research and Innovation, vol. 2, no. 2, pp. 26-35, 2025.

 

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Journal of Green Energy Research and Innovation
Volume 2, Issue 2
Spring 2025
Pages 26-35

  • Receive Date 12 December 2024
  • Revise Date 06 January 2025
  • Accept Date 18 January 2025
  • Publish Date 01 June 2025

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