Forecasts that sit next to your live data.

The problem

Most operators get forecasts from a third party in CSV or via a separate web portal, then manually paste them into a trading sheet. The forecast model isn't tuned to the site, the on-site lidar that would calibrate it lives in another silo, and nobody updates the bias correction more than once a quarter. Imbalance penalties are the result.

How WindTwin solves it

• Mesoscale forecast ingest — ECMWF (HRES & ENS), GFS, and DWD ICON, with hub-height interpolation per turbine.
• Site-tuned bias correction using your last 30–90 days of measured vs. forecast wind. Recomputed continuously, not quarterly.
• Met-mast and lidar fusion — on-site observations weighted into the short-term horizon (0–6 h) where they outperform the mesoscale.
• Power-output forecasts derived from your warranted power curve plus live turbine availability — so a curtailed turbine doesn't poison the portfolio number.
• Ensemble spread and confidence bands — the trading desk sees how confident the forecast is, not just the central value.
• Forecast vs. actual scorecards updated every day, so you can prove the value to the trading and treasury teams who fund the platform.

How it works

1. WindTwin pulls forecast grids (NetCDF / GRIB2) from the configured providers on each model run.
2. Wind speed and direction are interpolated to each turbine's hub height and bias-corrected against the site's measurement history.
3. Power output is derived per turbine using the warranted curve, then aggregated to farm and portfolio level — accounting for live availability.
4. Outputs land in the same dashboards as live SCADA, in the same API, and in the same exports.

What you can do with it

• Submit nominations and intra-day re-nominations from the same screen you operate the farm in.
• Plan maintenance windows around forecasted low-wind periods — without juggling two systems.
• Defend forecast performance to the trading desk with daily MAE/RMSE scorecards.