Probabilistic energy forecasting through quantile regression in reproducing kernel Hilbert spaces

TL;DR

This paper introduces a non-parametric kernel quantile regression method in reproducing kernel Hilbert spaces for probabilistic energy demand forecasting, emphasizing uncertainty quantification and benchmarking against existing methods.

Contribution

It presents a novel application of RKHS-based kernel quantile regression for energy forecasting, with a focus on reliability, sharpness, and reproducibility of results.

Findings

Demonstrates the method's reliability and sharpness in energy forecasting.

Benchmarks the approach against state-of-the-art methods.

Provides reproducible implementation and scripts.

Abstract

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.