EnPower offers an AI‑driven energy management platform that ingests real‑time telemetry from distributed solar assets, applies deep‑learning forecasts, and autonomously dispatches generation, storage, and grid interaction to maximize economic return and lower emissions. The SaaS solution provides hardware‑agnostic integration, a secure cloud dashboard, and open APIs for remote monitoring, analytics, and control.
Funding
Funding not disclosed
Founders
Product
Problem
Distributed solar installations often operate with static control logic, causing excess generation to be wasted, underutilizing assets, and increasing reliance on the grid. Without accurate forecasting and automated dispatch, owners see lower return on investment and higher carbon emissions. Integrating heterogeneous IoT devices and weather feeds in real time is also technically cumbersome.
Solution
EnPower delivers an AI‑driven energy management platform that continuously ingests telemetry from any solar asset, applies deep‑learning forecasts of production and load, and executes autonomous dispatch decisions. The optimization engine balances on‑site generation, storage, and grid interaction to maximize economic return while minimizing emissions. Results are streamed to a secure, cloud‑hosted dashboard that offers real‑time analytics, alerts, and remote control from any device. The system’s multi‑protocol stack enables plug‑and‑play integration with existing inverters, meters, and battery management systems without hardware redesign. By leveraging predictive analytics and adaptive scheduling, EnPower turns surplus solar output into measurable cost savings and energy autonomy for distributed portfolios.
Target Audience
Primary customers are owners and operators of distributed solar farms, commercial real‑estate energy managers, and utility‑scale solar aggregators seeking automated, data‑driven optimization of their assets.
Features
- Real‑time data ingestion from heterogeneous solar IoT devices via multi‑protocol, hardware‑agnostic connectors
- Deep‑learning forecasting models that combine historical production, weather APIs, and consumption patterns to predict net output with <5% error margin
- Optimization engine that performs continuous, constraint‑aware dispatch of generation, storage, and grid import/export to maximize ROI and reduce emissions
- Fully autonomous scheduling and control loop that eliminates manual operator intervention while maintaining safety and compliance limits
- Secure, SaaS‑based cloud platform with role‑based access, end‑to‑end encryption, and 99.9% uptime SLA
- Responsive web dashboard and mobile app delivering real‑time KPIs, trend analytics, and remote override capabilities
- Open REST/GraphQL API for seamless integration with enterprise energy management systems and ERP solutions