Probabilistic energy forecasting through quantile regression in reproducing kernel Hilbert spaces
This addresses uncertainty quantification for energy forecasting to support sustainable energy development, but it is incremental as it adapts an existing method to a specific domain.
The study tackled energy demand forecasting by applying kernel quantile regression in reproducing kernel Hilbert spaces to quantify uncertainty, demonstrating reliability and sharpness in load and price forecasting for the DACH region.
Accurate energy demand forecasting is crucial for sustainable and resilient energy development. To meet the Net Zero Representative Concentration Pathways (RCP) $4.5$ scenario in the DACH countries, increased renewable energy production, energy storage, and reduced commercial building consumption are needed. This scenario's success depends on hydroelectric capacity and climatic factors. Informed decisions require quantifying uncertainty in forecasts. This study explores a non-parametric method based on \emph{reproducing kernel Hilbert spaces (RKHS)}, known as kernel quantile regression, for energy prediction. Our experiments demonstrate its reliability and sharpness, and we benchmark it against state-of-the-art methods in load and price forecasting for the DACH region. We offer our implementation in conjunction with additional scripts to ensure the reproducibility of our research.