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.
arXiv:2411. 12193v4 Announce Type: replace-cross Abstract: The rapid growth of distributed energy resources (DERs) presents both opportunities and operational challenges for electric grid management.
arXiv:2607. 20587v1 Announce Type: cross Abstract: Modern power systems increasingly require probabilistic forecasts amid interacting uncertainties from renewable intermittency, flexible demand, market volatility, and weather-dependent generation.
arXiv:2510. 15780v2 Announce Type: replace-cross Abstract: Artificial intelligence (AI) is increasingly used to support renewable energy forecasting and grid operations.
Low-voltage load forecasting is an important component in current and future energy systems with a high degree of electrification and decentralized generation. However, current forecasting methods require significant manual effort, often lack uncertainty estimation and proper peak prediction, and they are often not adequately evaluated in terms of grid requirements.
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.
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: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 introduces rankECE, a new metric for assessing calibration error in predictive models. Unlike the widely used Expected Calibration Error (ECE), rankECE compares predictions with neighboring probability values, offering theoretical guarantees and empirical evidence that it better approximates ECE than traditional binned methods.
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. 01171v1 Announce Type: new Abstract: Sample-based generative models are increasingly used for probabilistic forecasting in high-stakes decision settings, yet their training objectives are blind to the decision maker's cost structure.
arXiv:2609.25788v1 Announce Type: new Abstract: Time Series Foundation Models (TSFMs) promise a paradigm shift toward zero-shot forecasting by eliminating task-specific training. However, existing wo...
arXiv:2605. 26703v2 Announce Type: replace-cross Abstract: The classic concept of "calibrated forecasts" and its more recent refinement, "calibeating," are defined with respect to the standard quadratic scoring rule.