The paper presents an online algorithm for multivariate distributional regression tailored to high‑dimensional probabilistic electricity price forecasting. It models conditional means, variances, and dependence structures of 24‑hour day‑ahead prices using coordinate descent and LASSO regularization, enabling scalable estimation in large covariate spaces. A regularized estimation path allows early stopping to prevent overfitting, and the method delivers interpretable, well‑calibrated joint prediction intervals, outperforming benchmarks on German market data.
arXiv:2607. 02623v1 Announce Type: new Abstract: Time series foundation models (TSFMs) have shown strong zero-shot forecasting performance, but their generalization in covariate-driven, non-stationary settings is underexplored.
The paper presents a hybrid neural architecture that blends linear and nonlinear feed‑forward networks for day‑ahead electricity price forecasting. It introduces a partial online learning strategy with warm‑starting and stage‑specific hyperparameters to cut computational time, and employs Bernstein Online Aggregation to combine forecasts. Experiments on six years of major European markets show the method reduces RMSE by 11‑12% and MAE by 14‑17% compared to state‑of‑the‑art benchmarks while lowering computational cost.
arXiv:2510. 16898v2 Announce Type: replace-cross Abstract: Accurate prediction of electricity prices is crucial for stakeholders in the energy market, particularly for grid operators, energy producers, and consumers.
arXiv:2609.23223v1 Announce Type: cross Abstract: Day-ahead electricity price forecasts support trading and storage decisions, but for battery arbitrage predicting intraday price spreads is more rele...
arXiv:2610.07232v1 Announce Type: new Abstract: Accurate short-term load forecasting (STLF) is essential for the reliable and efficient operation of modern power systems. While time series foundation...
The paper presents a comparative study of six deep learning models—state-space, MLP, RNN, and Transformer-based architectures—for cross-border electricity price forecasting using publicly available data. It focuses on generalization across markets and evaluates performance under low-data target-market conditions (zero-shot, one-shot, few-shot) with a standardized dataset for the Germany‑Luxembourg bidding zone in 2024. Results show that N‑HiTS and NBEATSx perform competitively in limited‑data scenarios, while transformer models achieve comparable accuracy but require more adaptation and tuning, and that careful feature selection and hyperparameter tuning improve performance.
While publicly available electricity market data presents a valuable resource for forecasting research, the field lacks established benchmark datasets for standardized comparison. As a result, many st...
arXiv:2405.07359v2 Announce Type: replace Abstract: Accurate prediction of electricity day-ahead prices is essential in competitive electricity markets. Although stationary electricity-price forecast...
arXiv:2604. 26634v2 Announce Type: replace Abstract: Norway's electricity market is heavily dominated by hydropower, but the 2021-2022 energy crisis and stronger integration with Continental Europe have fundamentally altered price formation, reducing the reliability of forecasting models calibrated on historical data.
The paper introduces two methods for modelling aggregated supply and demand curves in the EPEX SPOT Day‑Ahead market. The first is a low‑dimensional parametric approach that produces deterministic point forecasts using plateau levels, elastic‑region boundaries, polynomial coefficients, and XGBoost. The second is a high‑dimensional generative approach based on conditional Denoising Diffusion Probabilistic Models that samples plausible curves from price arrivals and volume‑increment marks, enabling analysis of price and volume sensitivity and price impact.
arXiv:2603. 15802v2 Announce Type: replace Abstract: In many time series forecasting settings, the target time series is accompanied by exogenous covariates, such as promotions and prices in retail demand; temperature in energy load; calendar and holiday indicators for traffic or sales; and grid load or fuel costs in electricity pricing.
The paper introduces Wasserstein Exponential Smoothing (WES), a single‑parameter recursive method for forecasting distributional time series on ℝ. WES updates forecast distributions along Wasserstein geodesics, allowing direct application to empirical distributions without parametric modeling. In high‑frequency equity‑index return and household electricity‑demand data, WES achieves the lowest one‑step‑ahead Wasserstein prediction error among existing benchmarks and is retained in the 90% model confidence set for all 20 series examined.