JoulesAI provides a cloud-native platform that uses reinforcement‑learning agents to autonomously control heterogeneous distributed energy resources, optimizing cost, carbon emissions, and resilience in real time. The API‑first solution integrates with existing EMS/SCADA systems, enabling data centers, campuses, airports, and industrial facilities to automate dispatch, demand response, and market participation under FERC Order 2222.
Funding
Funding not disclosed
Founders
Product
Problem
Distributed energy resources (DERs) at the grid edge are often managed with static rule‑sets and manual oversight, leading to suboptimal utilization, higher electricity costs, and difficulty meeting carbon and resiliency targets. The rapid growth of batteries, solar, EV fleets, and flexible loads outpaces the capabilities of legacy energy management software. Consequently, facilities struggle to adapt to volatile price signals and regulatory requirements in real time.
Solution
JoulesAI delivers autonomous, reinforcement‑learning agents that continuously learn and execute multi‑objective control policies across heterogeneous DER portfolios. The agents optimize for cost, carbon emissions, and operational resilience by dynamically dispatching batteries, solar inverters, generators, and flexible loads based on real‑time market data and facility constraints. A cloud‑native analytics layer aggregates sensor streams, runs predictive models, and provides actionable insights through an API‑first interface that can be embedded in existing EMS or SCADA systems. The platform supports decentralized decision‑making, enabling microgrids and large campuses to self‑balance while remaining compliant with market participation rules such as FERC Order 2222. Continuous online training ensures the control strategy evolves with changing tariffs, weather forecasts, and asset health, delivering measurable savings and reduced emissions without requiring extensive human intervention.
Target Audience
Primary customers are energy managers and operations teams at data centers, corporate campuses, airports, and industrial facilities that operate large DER fleets and need automated, real‑time optimization to meet cost, sustainability, and reliability goals. The solution also serves utilities and aggregators seeking to integrate customer‑site assets into wholesale markets.
Features
- Reinforcement‑learning control engine that optimizes cost, carbon, and resilience objectives across heterogeneous DERs in real time
- API‑first integration layer compatible with legacy EMS, SCADA, and third‑party market platforms for seamless dispatch coordination
- Predictive analytics module that forecasts load, generation, and price signals to inform proactive asset scheduling
- Automated demand‑response and peak‑shaving routines that react to volatile TOU rates and demand‑charge structures
- Fault detection and asset health monitoring using machine‑learning anomaly detection on equipment telemetry
- Scalable cloud architecture supporting thousands of nodes, from single‑site microgrids to multi‑facility campus networks
- Secure data pipeline with end‑to‑end encryption and role‑based access controls, meeting industry compliance standards
- Built‑in support for market participation under FERC Order 2222, enabling aggregated DER bidding in wholesale markets