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

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

Applied Energy, 2024, vol. 361, issue C, No S0306261924002836

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.

Keywords: Regional wind power forecasting; Quantile forecasting; Convolutional Neural Networks (CNN); Prediction distribution; Conformal predictive distribution; Quantile regression forest (search for similar items in EconPapers)
Date: 2024
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Citations: View citations in EconPapers (3)

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DOI: 10.1016/j.apenergy.2024.122900

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