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The research aims to create a hybrid framework for selecting optimal sites for wind farms in Iran using machine learning and decision-making methods.

June 4, 2026ArrayOpen Access

A nationwide hybrid machine learning and multi-criteria decision-making framework for robust and scalable wind farm site selection

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Randomized trial develops a robust framework for site selection in Iran, suggesting new suitable regions for wind energy.

The research aims to create a hybrid framework for selecting optimal sites for wind farms in Iran using machine learning and decision-making methods.
Developed a hybrid ML and MCDM framework for wind-site assessment.
Preprocessed data from 55 candidate regions using UMAP for dimensionality reduction.
Applied K-Means++ clustering and evaluated results with multiple MCDM techniques.
Population density emerged as the most significant criterion in site selection.
Wind speed at 10 m was found to have the lowest weight.
Regions like Zabol, Zahak, Moalleman, and Ahar ranked as the most suitable sites with wind power densities above 150 W/m².

Aghapour et al. (2026) studied this question.

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