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Improving Wind and Solar Power Prediction with Efficient Wrapper-based Feature Selection: An Empirical Study

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TL;DR — An empirical ML-methods paper proposing Cluster-based Sequential Feature Selection (CSFS), a model-agnostic wrapper method for picking input features in renewable energy (wind/solar) power prediction pipelines. It matters because it offers a systematic, cheaper alternative to ad-hoc feature selection in a domain with many monitoring variables.

  • Motivated by two structured literature reviews (wind turbine power-curve modeling; PV power prediction) finding that feature selection is currently limited or unsystematic despite many available variables.
  • CSFS is a clustering-based wrapper method aimed at automatic, efficient, reliable feature selection; open-source implementation provided on GitHub.
  • Benchmarked against sequential feature selection (SFS), filter-based methods, and Random Forest embedded importance; wrapper-based methods gave overall better feature selections.
  • CSFS matches SFS predictive performance while cutting computational cost by ~21% on average.

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Improving Wind and Solar Power Prediction with Efficient Wrapper-based Feature Selection: An Empirical Study

arXiv cs.LG Daniel Grillmeyer, Marius Hadry, Michael Stenger, Vanessa Borst, Veronika Lesch, Samuel Kounev 2026-07-15 arXiv:2607.14024
Public signals Semantic Scholar citations 0 · Semantic Scholar influential citations 0
Providers: Hugging Face · N/A OpenAlex · N/A Publisher · N/A Semantic Scholar · Citations 0 · Influential citations 0 X · N/A Fetched 2026-08-07 09:45:15.309088 UTC

TL;DR — An empirical ML-methods paper proposing Cluster-based Sequential Feature Selection (CSFS), a model-agnostic wrapper method for picking input features in renewable energy (wind/solar) power prediction pipelines. It matters because it offers a systematic, cheaper alternative to ad-hoc feature selection in a domain with many monitoring variables.

  • Motivated by two structured literature reviews (wind turbine power-curve modeling; PV power prediction) finding that feature selection is currently limited or unsystematic despite many available variables.
  • CSFS is a clustering-based wrapper method aimed at automatic, efficient, reliable feature selection; open-source implementation provided on GitHub.
  • Benchmarked against sequential feature selection (SFS), filter-based methods, and Random Forest embedded importance; wrapper-based methods gave overall better feature selections.
  • CSFS matches SFS predictive performance while cutting computational cost by ~21% on average.
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