Central Stars

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  App's Output

Central Stars was built for Track 1 because renewable energy is often wasted when solar and wind overproduce on certain days and the grid can’t absorb it, while on other days output drops and operators are forced to rely on non-renewable backup power; our solution is a web app that takes a user-provided location/region, pulls a 48-hour weather forecast, and converts those weather conditions into hour-by-hour solar and wind wattage predictions with uncertainty/confidence so users can make storage decisions ahead of time—charging batteries or other storage during forecasted surplus windows to reduce curtailment, and discharging stored energy during forecasted low-renewable windows to maintain reliability; we designed the product around an operational loop (location → forecast → exceedance detection → recommended charge/discharge actions), focused on making assumptions transparent since true output depends on site-specific factors like capacity, turbine curves, and panel configuration, and our main challenges were translating weather into power without plant metadata, handling wind forecast volatility with confidence indicators, aligning time zones/formats from weather data sources, and avoiding fake precision on demand by using editable demand assumptions; overall, we learned that the real value is pairing forecasts with actionable timing decisions and clear uncertainty, and the intended impact is less renewable curtailment, smarter storage use, and more consistent clean energy availability even when weather changes.

Built With

• google-ai