CSFS reduces wind and solar power prediction feature selection cost by 21%

Researchers propose Cluster-based Sequential Feature Selection (CSFS), a novel clustering-based wrapper method for automatic and efficient feature selection in renewable energy prediction pipelines. The approach addresses the lack of systematic methods for selecting input features from the large number of available monitoring and environmental variables.

• CSFS is model-agnostic and designed to improve reliability in wind turbine power curve modeling and photovoltaic power prediction.
• An open-source implementation is provided on GitHub to support reproducibility and reuse.
• Empirical evaluation compares CSFS against wrapper-based sequential feature selection (SFS), filter-based methods, and Random Forest's embedded feature importance.
• Wrapper-based methods generally provide better-performing selections of features compared to other techniques.
• CSFS achieves predictive performance comparable to SFS while reducing computational cost by an average of 21%.

The study highlights that wrapper-based methods offer superior feature selection performance, with CSFS providing a more efficient alternative to existing sequential approaches without sacrificing accuracy.