htm-monitor is an unsupervised early-warning monitor for critical systems, built to stay quiet until something matters. Its power grid case studies are scored against documented emergencies under one published standard, with every alert shown.
Each case study replays about six years of public hourly grid data and scores every alert against emergencies with documented onset times. The engine itself takes any continuous numeric signal; results are published only where they have been scored.
Six years of hourly ERCOT data, scored against Winter Storm Uri. 1 of 1 target event alerted inside the window (103 h lead). 2 alerts in 6.2 years, 0 unexplained.
Six years of hourly CAISO data, scored against three declared grid emergencies. 3 of 3 target events alerted inside the window (1.6 h lead, <1 h lag, 136 h lead). 19 alerts in 6.2 years, 8 unexplained.
htm-monitor has been replayed over about six years of public grid data per region and scored against documented emergencies. Here is what it found, including the alerts that matched nothing.
Early warning of grid emergencies from hourly demand and generation data
| Event | Outcome | Lead / Lag | First Alert (UTC) | Onset (UTC) |
|---|---|---|---|---|
| Winter Storm Uri Rotating outages, up to 20,000 MW of load shed |
Predicted | 103 h lead | 2021-02-11 00:00 | 2021-02-15 07:20 ERCOT entered Emergency Operations Level 3 (EEA 3) and began rotating outages · source |
| Event | Outcome | Lead / Lag | First Alert (UTC) | Onset (UTC) |
|---|---|---|---|---|
| August 2020 Rolling Blackouts Stage 3 Emergency, rotating outages on Aug 14 and 15 |
Predicted | 1.6 h lead | 2020-08-15 00:00 | 2020-08-15 01:38 CAISO declared a Stage 3 Emergency · source |
| September 2020 Labor Day Heat Wave Stage 2 Emergency, no outages ordered |
Detected | <1 h lag | 2020-09-07 01:00 | 2020-09-07 00:55 CAISO declared a statewide Stage 2 Emergency · source |
| September 2022 Record Demand EEA 3 declared, no load shed ordered by CAISO |
Predicted | 136 h lead | 2022-09-01 08:00 | 2022-09-07 00:17 CAISO declared an EEA 3 · source |
The engine is Hierarchical Temporal Memory — a biologically-inspired sequence-learning algorithm extended with a grouped-consensus decision layer.
Each signal model learns its temporal patterns continuously and adapts to drift without a retraining cycle. No static limits on the raw signal values. No supervised labels.
A system alert fires only when several signal groups are hot at the same time and stay that way for consecutive hours. The consensus layer is there to keep single-signal noise from becoming an alarm.
Every alert is traceable to specific signal anomalies at specific timesteps. Per-model, per-group visibility lets operators understand exactly what triggered and why.
Runs single-process in Python on a CPU. Scoring needs no external services, so it can run on a small VPS or inside an air-gapped VM.
Most anomaly detection systems generate noise. htm-monitor is built for the opposite: say nothing until something genuinely matters, then say it early enough to act. Foresight is power.
We believe the same unsupervised temporal learning engine can serve any system that produces continuous numeric data: learn what normal looks like, and flag when reality departs from it. That is a design goal, not yet a result. The scored case studies on this site are power grids.
A system that cries wolf loses trust. htm-monitor is designed for rare, high-confidence alerts — not dashboard noise.
No labeled training data required. The system learns from the structure of normal operations and flags departures automatically.
Every alert is fully auditable, traceable to specific signals and timesteps. Operators can see exactly what the system sees.
The engine takes any continuous numeric time series and makes no grid-specific assumptions. Published results cover only what has been scored.
htm-monitor is developed and operated by DataGrip LLC. We're focused on making unsupervised temporal anomaly detection practical and accessible for critical infrastructure operators.
Whether you want a free technical preview, a paid pilot, or just want to learn more — we'd love to hear from you.
Send a 90-day telemetry sample and one past event you'd like scored. You get a preview audit within a week: lead/lag timing, false-positive rate, per-signal analysis. No commitment.
Full historical replay against your event catalog, with a written per-event audit. The fee credits in full against your first subscription month.