SolarPower ML
Solar Power Forecasting Powered by Physics-Aware AI
6 followers
Solar Power Forecasting Powered by Physics-Aware AI
6 followers
Passive monitoring is obsolete. SolarPower ML is the active intelligence layer for your energy stack. Using physics-informed machine learning and satellite telemetry, we model atmospheric opacity to forecast yield days in advance. Designed for prosumers who demand scientific precision, we automate battery scheduling to secure true energy independence. The future of the smart grid is predictive.
Hi Product Hunt!
We built SolarPower ML because the current state of energy software is surprisingly "dumb."
Your expensive solar panels and smart batteries are likely running on basic, reactive logic: If sun shines → charge battery.
That wasn't good enough. We wanted to move the industry from Passive Monitoring (looking at what happened yesterday) to Active Forecasting (predicting what will happen tomorrow).
SolarPower ML is the intelligence layer for the new energy stack.
Whether you are optimizing a single smart home or managing a fleet of thousands, the physics are the same. We just made them accessible.
The Tech Stack:
Physics-Informed AI: We don't rely on black-box ML. We use hybrid models grounded in Clearsky physics to predict generation up to 96 hours out.
Satellite Telemetry: Real-time ingestion of weather forecast data means we see the clouds before they hit the panels.
Dual-Scale Architecture:
For Prosumers: It’s a personal data scientist. Maximize self-consumption and battery ROI automatically without spreadsheets.
For Providers: It’s a VPP-ready platform. Aggregate thousands of nodes with enterprise-grade security (End-to-End Encryption + RBAC) to stabilize the grid.
We are live in v0.4 Beta. We want to prove that "Scientific Precision" belongs in consumer energy apps.
The Question: Do you trust an AI to manage your home's energy yet?
Let’s chat below!