Short-term load forecasting under EU-AI Act Requirements in Safety-Critical Environments: Results from a 41-day live challenge on the aggregated German transmission-grid load
Thomas Bartz-Beielstein, Inalbek Akiev, Lalo Mohamad
cs.AI, cs.LG
Submitted: 2026-08-13
Updated: 2026-08-14
Comments: Version 3. 40 pages
Code: https://github.com/bartzbeielstein/challenge-leaderboard
Project page: https://bartzbeielstein.github.io/challenge-leaderboard
License: http://creativecommons.org/licenses/by-nc-nd/4.0/
The gist: Short-term load forecasting (STLF) plays a vital role in the electric power industry.
Terminology
Abstract
Short-term load forecasting (STLF) plays a vital role in the electric power industry. It is relevant for critical infrastructure. STLF is no longer purely a performance and accuracy problem, because determinism, fail-safe handling, minimal-attack surface, and no dead code are software-engineering requirements rather than optional extras. This report describes results from a 41-day live challenge that evaluated an STLF pipeline for the aggregated German transmission-grid load. The STLF pipeline predicts the 24 hourly load values of a target day from European Network of Transmission System Operators for Electricity (ENTSO-E) data. It is based on the open-source Python library spotforecast2-safe, which tries to implement the EU-AI Act Requirements in Safety-Critical Environments by design. The STLF pipeline includes gap- and anomaly-aware data preprocessing, calendar and weather covariates, and a forecasting algorithm. Forecast quality is compared to the official ENTSO-E day-ahead forecast. The spotforecast2-safe pipeline beats the ENTSO-E baseline. In-context models show competitive performance. Transparent, deterministic, low-cost, and auditable local models (referred to as macl2l in this report) are competitive with more than 100-million-parameter large, energy-intensive pre-trained foundation models such as chronos-2. The challenge implementation, the submission history of all teams, and the archived leaderboard are publicly available.
Sources
- Chronos-2: From Univariate to Universal Forecasting
- Chronos: Learning the Language of Time Series
- Optimization with SpotOptim
- Time-Series Forecasting in Safety-Critical Environments: An Open-Source Package for EU-AI-Act-Compliant Development / Zeitreihenprognose in sicherheitskritischen Umgebungen: Ein Open-Source-Paket f\"ur die KI-VO-konforme Entwicklung
- Do We Really Need Deep Learning Models for Time Series Forecasting?
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