Moose Energy AI delivers lightweight, low-cost photovoltaic monitoring powered by machine learning, digital twin simulation, and real-time anomaly detection — so you catch problems before they cost you.
From rooftop residential installs to commercial arrays, Moose monitors performance, models expected output, and flags real issues — intelligently.
Our machine learning engine learns your system's unique performance fingerprint and accurately flags only genuine issues — minimizing noise and false positives.
A real-time virtual model of your solar installation simulates expected output using live weather, irradiance, and historical data — so deviations are immediately visible.
Receive instant notifications when performance drops below modeled expectations. Prioritized by severity so your team acts on what matters most first.
Ingests data from inverter APIs, weather services, grid sensors, and IoT devices — building a rich, unified picture of system health without expensive hardware upgrades.
Move beyond reactive fixes. Moose identifies degradation trends early, giving maintenance teams actionable lead time to prevent costly failures.
Designed from the ground up to run efficiently on modest infrastructure — making professional-grade solar intelligence accessible without enterprise pricing.
Link your inverter, weather APIs, and sensor data sources. Moose handles the integration layer so you don't have to.
The digital twin engine builds a live simulation of your array — calculating what optimal output should look like given current conditions.
ML algorithms train on your system's historical patterns to distinguish normal variance from genuine underperformance.
When real anomalies are detected, you receive a prioritized, actionable alert — not a wall of noise.
Moose Energy AI leverages best-in-class cloud and AI tools to deliver a scalable, reliable monitoring platform for solar operators of all sizes.
We're onboarding early partners now. Join the waitlist and get priority access to the Moose Energy AI monitoring platform.