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A novel day-ahead regional and probabilistic wind power forecasting framework using deep CNNs and conformalized regression forests

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  • Jonkers, Jef
  • Avendano, Diego Nieves
  • Van Wallendael, Glenn
  • Van Hoecke, Sofie

Abstract

Regional forecasting is crucial for a balanced energy delivery system and for achieving the global transition to clean energy. However, regional wind forecasting is challenging due to uncertain weather prediction and its high dimensional nature. Most solutions are limited to single-turbine or farm/park forecasting; therefore, this work proposes a day-ahead regional wind power forecasting framework using deep Convolutional Neural Networks (CNN) with context-aware turbine maps and Conformal Quantile Regression (CQR) to generate quantile forecasts with valid coverage.

Suggested Citation

  • Jonkers, Jef & Avendano, Diego Nieves & Van Wallendael, Glenn & Van Hoecke, Sofie, 2024. "A novel day-ahead regional and probabilistic wind power forecasting framework using deep CNNs and conformalized regression forests," Applied Energy, Elsevier, vol. 361(C).
    • Handle: RePEc:eee:appene:v:361:y:2024:i:c:s0306261924002836
    DOI: 10.1016/j.apenergy.2024.122900
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    References listed on IDEAS

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