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.
By Hadeer Elashhab, Sai Srijan Papineni, Marvin Dorn, Veit Hagenmeyer, Benjamin Sch\"afer
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: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.
By My Thi Diem Phan, Trung Tuyen Truong, Hoai Phuong Ha, Dat Thanh Nguyen
The paper presents a Causal Graph‑Informed Temporal Convolutional Network (CG‑TCN) that fuses a learned causal graph with a temporal convolutional network to forecast retail electricity prices. By decomposing price series into multi‑resolution trends and discovering a causal graph over these components and key covariates, the model conditions its convolutions and attention on causal pathways. On ten years of Ohio residential contracts, CG‑TCN outperforms benchmarks, achieving MAEps of 3.08%, 3.82%, and 5.43% for one‑, ten‑, and fifteen‑step‑ahead forecasts, respectively.
By Yufan Ji, Abdollah Shafieezadeh, Noah Dormady
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...
By Antonio Malpica-Morales, Miguel A. Dur\'an-Olivencia, Serafim Kalliadasis
arXiv:2608. 13039v1 Announce Type: new Abstract: The paper introduces the ante-hoc Explainable AI methodology to assess the global feature importance of the Machine Learning models used for heat demand forecasting in intelligent control of District Heating Systems, with motivation to facilitate their interpretability and trustworthiness, hence addressing the challenges related to adherence to communal standards, customer satisfaction and liability risks.
By Milan Zdravkovi\'c
The paper proposes a neural‑network algorithm for contextual electricity pricing, framing the problem as a Stackelberg game and using a mean‑field solution to learn constrained mappings from contextual features (e.g., weather, sunrise/sunset, day‑of‑week) to feasible price signals. The method is validated through simulations of the energy grid in several U.S. cities, demonstrating that incorporating rich contextual information can significantly enhance the value of demand‑response programs.
By Jing Shang, Mohammad Mehrabi, Xinyang Zhou, Mahmoud Saleh, Andrey Bernstein, Stefan Wager
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.
By Btissame El Mahtout, Florian Ziel
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.
By Salih Salihoglu, Ibrahim Ahmed, Afshin Asadi
arXiv:2609.06085v1 Announce Type: cross
Abstract: Understanding the joint dynamics of prices and trades is central to market microstructure, where returns and order flow interact through nonlinear an...
By Manuel Naviglio, Fabrizio Lillo
The study evaluates nine foundation model variants against two leading electricity price forecasting benchmarks across Germany, Poland, and Spain for 2021‑2025. Only the TabPFN models consistently outperform the benchmarks in both point and probabilistic accuracy, yet their economic advantage varies with bidding strategy and risk tolerance. The results indicate that foundation models cannot universally replace market‑specific models; their usefulness depends on the chosen architecture and the particular decision problem.
By Arkadiusz Lipiecki, Rafa{\l} Weron
arXiv:2608. 12363v1 Announce Type: cross Abstract: European countries are debating policies to mitigate the increased energy costs caused by renewed geopolitical tensions, while pursuing decarbonization and electrification.
By Javier Gonzalez-Ruiz, Carlos Rodriguez-Pardo, Alice Di Bella, Paolo Mastropietro, Jose Pablo Chavez-Avila, Massimo Tavoni