Power & Wind
Wasted energy is wasted money. Fix both.
Better short-horizon load and production outlooks, clearer underperformance on rotating and balance-of-plant kit, and reporting that does not live in twelve spreadsheets.
The Problem
Loads move fast. Your plan should too.
Demand and renewable output swing inside the hour. Aging plant and turbines drift; the gap often shows up only when the month is closed.
Sustainability and grid rules need numbers on demand. Ops is still reconciling SCADA exports while the board asks for next quarter.
You are not short on telemetry — you are short on a single forecast and performance layer everyone trusts.
How it works
One layer on top of SCADA and EMS.
Twin critical assets
Turbines, generators, and key grid segments — state that reflects weather, wear, and how you have actually dispatched.
Forecast load and output
Weather, seasonality, and history combined for horizons your traders and schedulers actually use.
Surface underperformance
Pitch, loading, maintenance windows — ranked so you fix what moves MWh first.
Automate sustainability views
Emissions and intensity tracked from the same stream you operate on — fewer manual reconciliations before filings.
Results
What teams measure once it's in production.
- 20–50%
- boost in operational efficiency and asset lifespan
- More accurate
- demand forecasting — reducing overproduction and energy waste
- Automated
- sustainability reporting — aligned with regulatory requirements
Who this fits
Who this fits
Ops directors, site managers, and sustainability leads at utilities and wind who need forecasts and asset insight that survive a control-room review.
Straight talk
Common questions before a PO.
- "We already have a SCADA system and dashboards."
- So do all our clients. We don't replace your SCADA — we sit on top of it and turn that raw data into predictions and recommendations your current setup can't provide.
- "Wind is inherently unpredictable."
- It is. That's why our models combine weather data, historical patterns, and physics simulations to give you the best possible forecast — not certainty, but a massive improvement over guessing.
- "We need this across multiple sites."
- Built for it. Federated learning lets us train models across distributed locations without centralizing sensitive data. Scale without compromising privacy.
Next step
See where MWh and margin are leaking.
Half an hour on your asset mix: the twin boundary, the first forecasts, and what we would prioritize for output.