Probabilistic energy forecasting through quantile regression in reproducing kernel Hilbert spaces

Luca Pernigo, Rohan Sen, Davide Baroli
8/8/2024
cs.LGcs.AIeess.SY

No official code found. Best implementation: luca-pernigo/kernel_quantile_regression (8 stars, MIT, last commit 2024-08-14).

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.

View on arXivView PDF

Code Implementations(1)

luca-pernigo/kernel_quantile_regressionCommunity100%
MIT

Kernel quantile regression

82Shell, PythonApr 12, 20242 years agoMIT
confidence-intervalsenergy-forecastingkernel-methodsprobabilistic-forecastingpython+1 more

Similar Papers

Isotonic Quantile Regression Averaging for uncertainty quantification of electricity price forecasts

81% match2025

Any-Quantile Probabilistic Forecasting of Short-Term Electricity Demand

80% match1 repo62024

Efficient pooling of predictions via kernel embeddings

78% match2024

Analyzing Uncertainty Quantification in Statistical and Deep Learning Models for Probabilistic Electricity Price Forecasting

78% match2025

Stacking for Probabilistic Short-term Load Forecasting

78% match2024

Probabilistic load forecasting with Reservoir Computing

78% match1 repo102023

Cite this paper

@article{pernigo2024probabilistic,
  title  = {Probabilistic energy forecasting through quantile regression in reproducing kernel Hilbert spaces},
  author = {Luca Pernigo and Rohan Sen and Davide Baroli},
  year   = {2024},
  eprint = {2408.04405},
  archivePrefix = {arXiv},
  url    = {http://arxiv.org/abs/2408.04405v3}
}

Add a badge to your README

Shows whether official code is linked for this paper.

Code of Paper badge
[![Code of Paper](https://codeofpaper.com/badge/2408.04405.svg)](https://codeofpaper.com/paper/2408.04405)

Discussion