CSFS reduces computational cost by 21% in renewable energy prediction

Researchers propose Cluster-based Sequential Feature Selection (CSFS), a novel, model-agnostic wrapper method designed to address the lack of systematic feature selection in wind and solar power prediction. The approach aims to provide automatic, efficient, and reliable feature selection for renewable energy pipelines.

• CSFS is evaluated on wind turbine power curve modeling and photovoltaic power prediction.
• It is compared against established techniques including wrapper-based sequential feature selection (SFS), filter-based methods, and Random Forest's embedded feature importance.
• Results indicate that wrapper-based methods generally provide better-performing feature selections.
• CSFS achieves predictive performance comparable to SFS while reducing computational cost by an average of 21%.

The authors provide an open-source implementation on GitHub to support reproducibility and reuse in renewable energy prediction tasks.