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Optimal Siting of Wind Turbines Using Machine Learning (Case Study: Rudbar County)
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Hossein Barekati *   , Mohammadreza Jelokhani Niaraki |
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Abstract: (44 Views) |
The uneven distribution of wind energy across the Earth's surface makes selecting suitable locations for wind farms one of the most critical stages in wind power plant design. This complex process requires consideration of multiple environmental, economic, and social factors. Multi-criteria decision-making approaches face numerous challenges in this regard. To address these challenges, the integration of machine learning techniques has emerged as an innovative approach within Geographic Information Systems (GIS).
This study presents a machine learning (ML) and GIS-based framework for the automated selection of wind farm sites. Three machine learning algorithms were employed in this framework. Rudbar County was chosen as the study area due to the presence of the Manjil site, which constitutes a significant portion of Iran's installed wind energy capacity.
Among the ML models evaluated, the Gradient Boosting model demonstrated the highest performance with an accuracy of 97%, followed by the Extreme Gradient Boosting and Random Forest models. SHAP (SHapley Additive exPlanations) analysis revealed that wind speed, distance to transmission lines, proximity to protected areas, and elevation were the most influential criteria in selecting suitable wind farm locations in this region.
In Rudbar County, high-potential areas were identified that remain unequipped with wind turbines. These findings highlight the significance of these sites for future investment in wind farm development. The proposed framework can be effectively applied to wind farm site selection studies in provinces with similar conditions. |
Article number: 2 |
| Keywords: Wind Power Plant, Geographic Information System (GIS), Machine Learning (ML), SHAP Method |
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Full-Text [PDF 1097 kb]
(19 Downloads)
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Type of Study: Research |
Subject:
GIS
Received: 2024/08/27
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