Energy & Utilities
Renewable Energy Grid Forecasting
AirflowSnowflakePythonTimescaleDB
The Challenge
A utility company integrating solar and wind into their grid struggled with supply forecasting. Renewable output varies by weather and time of day, causing either costly overproduction sold at a loss or dangerous supply shortfalls requiring emergency gas peaker plants.
The Approach
Built a forecasting pipeline ingesting satellite imagery, weather station data, and historical generation curves from 340 renewable sites. Time-series models produced 15-minute-interval generation forecasts 48 hours ahead, feeding directly into the grid dispatch system.
The Impact
Forecast accuracy improved from 68% to 94%
Peaker plant activations reduced by 41%
340 renewable sites monitored in real time
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