TRACE is a reproducible benchmark for electricity price forecasting that pairs 7,300 zone–day instances from five U.S. market zones with official operational text available at the forecast cutoff, ensuring no post‑cutoff information leakage. The benchmark evaluates semantic alignment and forecasting value, showing that the text aligns with central movement and tail risks in ground‑truth prices, especially for upper‑tail risk. Forecasting models using TRACE’s text achieve a median 7.4% reduction in upper‑tail pinball loss, while a controlled ablation that mismatches text reverses these gains.
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: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.
arXiv:2610.17387v1 Announce Type: cross Abstract: Day-ahead (DA) electricity price forecasting is usually posed per market, with a separate model fitted to each grid's own history. This caps accuracy...
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.
arXiv:2606. 09517v1 Announce Type: new Abstract: As renewable energy integration increases market volatility, probabilistic electricity price forecasting has become essential for effective risk management.
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.
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.
CastClaw is an interactive forecasting system that combines an autonomous forecasting loop with a forecasting-oriented harness. Users define and refine forecasting tasks in natural language via command-line or Web interfaces, while an internal large language model agent selects tool actions or requests human feedback, using checked results to guide the forecasting process. The system integrates tool execution, state management, and validation, and has been evaluated on five public electricity‑price datasets, outperforming 16 baselines in point‑estimate MSE and MAE, with case studies on Nord Pool and North China electricity‑load data.
arXiv:2606. 24950v1 Announce Type: new Abstract: Financial decision-making is contextual: forecasting prices, valuing companies, and assessing event exposure weigh price history, accounting fundamentals, macroeconomic regime, and contemporaneous text.
arXiv:2607. 01966v1 Announce Type: new Abstract: Low-voltage load forecasting is an important component in current and future energy systems with a high degree of electrification and decentralized generation.
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...
Real-world time-series forecasting is rarely a one-shot model invocation: practitioners must formulate tasks, connect data and models, incorporate domain expertise, assess prediction plausibility, and...