  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.
