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In an effort to gain insight into Bangladesh’s great wind energy potential, this study is the very first of this sort that uses an advanced machine learning technique for a nationwide resources assessment. By applying a Self-Organizing Map for assessing and analyzing the resistance to wind of each of the 64 districts, we exceeded conventional localized studies. The results clearly indicate an exclusive geographical advantage, which has the southern coastal strip chosen as the best area for potential wind farm development. Through this work, making the strategy to begin via wind energy in the country. This will make it simpler to get funds as well as make future choices about the energy sector.

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Nationwide Wind Energy Resource Mapping in Bangladesh Using Self-Organizing Maps (SOM): A Machine Learning-Based Site Suitability Assessment

  • 会议论文

  • 发表日期：

    2026年

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  • Tonmoy Paul,
  • Pritam Sarkar

摘要

In an effort to gain insight into Bangladesh’s great wind energy potential, this study is the very first of this sort that uses an advanced machine learning technique for a nationwide resources assessment. By applying a Self-Organizing Map for assessing and analyzing the resistance to wind of each of the 64 districts, we exceeded conventional localized studies. The results clearly indicate an exclusive geographical advantage, which has the southern coastal strip chosen as the best area for potential wind farm development. Through this work, making the strategy to begin via wind energy in the country. This will make it simpler to get funds as well as make future choices about the energy sector.