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The increasing concern about climate change has contributed to promoting renewable energy technologies. Mini-eolic turbines are a common solution for domestic energy supply self-consumption installations. Due to this technology’s strong dependency on climate conditions and corresponding variability, it is important to develop intelligent systems to model and estimate its behavior. This paper uses three different feature selection methods, a clustering algorithm, and a regression technique to predict the power generated by a small wind turbine located in a bioclimatic house. Different configurations are tested, evaluating the impact on the model performance.

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Feature Importance Analysis of Meteorological Weather for Mini Eolic Electrical Power Prediction Using Clustering Information

  • 会议论文

  • 发表日期：

    2025年

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  • María Teresa García-Ordás,
  • Paula Arcano-Bea,
  • Manuel Rubiños,
  • Esteban Jove,
  • Diego Narciandi-Rodriguez,
  • Héctor Alaiz-Moretón

摘要

The increasing concern about climate change has contributed to promoting renewable energy technologies. Mini-eolic turbines are a common solution for domestic energy supply self-consumption installations. Due to this technology’s strong dependency on climate conditions and corresponding variability, it is important to develop intelligent systems to model and estimate its behavior. This paper uses three different feature selection methods, a clustering algorithm, and a regression technique to predict the power generated by a small wind turbine located in a bioclimatic house. Different configurations are tested, evaluating the impact on the model performance.