Model card for the Heimdall thesis (BSc Software Engineering, SDU Sønderborg,
with Danfoss A/S as industrial partner) by Phongsakon Mark Konrad and Tim Lukas
Adam. This repository holds the forecaster checkpoints and serves as the project
landing page.
The power grid keeps one promise every second, that supply matches demand, and
the balancing market is where that promise is settled in real time. As machine
learning models and large language models start to take part in this market,
they bring failure modes of their own. A forecasting model hides its own
uncertainty, and a language model can hallucinate. Heimdall is the safety guard
that sits between an autonomous bidder and the grid. It uses conformal
prediction to turn an uncertain price forecast into a promise the operator can
act on, that an accepted bid stays above their loss limit with the coverage
they asked for.
We run three kinds of bidder behind the guard, a deterministic chooser, a
single language model, and a multi-persona society. We report honestly that the
language model does not beat the deterministic baseline on profit. What it adds
is auditability, because every accepted and rejected bid carries its reasoning,
and steerability, because the society answers to plain-language instructions.
The guard holds its coverage to within a fraction of a percent of the 90 percent
target, including straight through the March 2025 rule change that reshaped the
market. Every number reproduces from real DK1 panels and real model traces.
The weights predict 15-minute imbalance prices for the Nordic mFRR EAM in
bidding zone DK1 under the post-2025-03-04 single-price regime. The reproducible
validation and test metrics, predictions, leaderboard, and training logs live
in the source repository (phomarkon/heimdall-src).
