The average is not where grids break.
GERT is built around the operational question conventional load dashboards obscure: how close is an unlikely—but plausible—demand tail to the system boundary?
Observe
Official load, adequacy and weather context enter with explicit provenance.
Forecast
Quantile models estimate a distribution from P50 through the extreme P99 tail.
Stress
Scenario controls perturb physical drivers through the same inference contract.
Prove
Calibration gates, artifact identity and source status remain visible to the operator.
A single expected-load line
→A visible P50–P99 uncertainty geometry
Weather conditions as the endpoint
→Weather translated into capacity-tail pressure
A black-box risk badge
→Risk logic, source provenance and artifact identity
A read-only monitoring dashboard
→Monitor → stress → replay → validate workflow
Tail demand
minus capacity
equals exposure.
P50 / P90 / P95 / P99
One hour ahead
A model is not “real” because it finished training.
Production promotion requires held-out calibration and data-quality gates. A failed candidate remains a candidate; the interface exposes fallback and stub states instead of silently upgrading their authority.
Decision support, not autonomous control.
GERT is a research and demonstration system. Any stub, fallback or uncalibrated output is labeled and must not be used for real grid operations. Operational deployment requires validated live integrations, governance and human authorization.