Gina Shaw Profile

Forecasting energy generation—especially from solar—has always been a tricky problem. Weather conditions change rapidly. Sensors behave unpredictably. And operational decisions often rely on assumptions rather than accurate forecasts.

Recently, we worked on a use case aimed at bringing more structure to this uncertainty. The goal was simple: predict solar power output up to three days in advance using historical data, real-time weather, and a bit of machine learning. What made this project interesting was how everything came together through Microsoft Fabric.