SolarPower ML – Physics-Informed AI for Solar Power Forecasting

Summary: SolarPower ML uses physics-informed machine learning and satellite telemetry to model atmospheric opacity and forecast solar energy yield up to 96 hours ahead. It automates battery scheduling for prosumers and supports large-scale energy management with enterprise-grade security.

What it does

It combines Clearsky physics-based hybrid models with real-time weather data to predict solar generation days in advance. The platform automates battery use and aggregates distributed energy resources for grid stabilization.

Who it's for

SolarPower ML targets prosumers seeking precise energy management and providers managing fleets or virtual power plants requiring secure aggregation.

Why it matters

It replaces reactive energy software with predictive intelligence, enabling optimized self-consumption and improved battery ROI through advanced forecasting.