Power your plants with AI

Real-Time Stability Management: Uses neural networks to analyze vast streams of data from smart meters and sensors to detect faults or disruptions instantly.
Self-Healing Rerouting: Automatically reroutes power during localized outages to minimize service interruptions and improve overall reliability.
Predictive Maintenance: Analyzes historical usage and environmental data to predict equipment failures (like transformer issues or wind turbine fatigue) before they occur.

Asset Aggregation: Combines distributed energy resources (DERs)—such as home solar panels, community batteries, and electric vehicles—to function as a single, flexible power plant.
Intelligent Balancing: Dynamically manages the discharge of stored energy during peak hours, reducing reliance on carbon-heavy "peaker" plants.
Intermittent Integration: Uses machine learning to forecast solar and wind output based on micro-weather patterns, smoothing the integration of volatile renewables into the main grid.

Autonomous Bidding: Executes high-speed, intraday trades for emissions allowances, renewable certificates, or excess stored power to maximize profitability.
Risk-Aware Decisioning: Leverages reinforcement learning to simulate market scenarios and volatility, making data-driven decisions that outpace manual trading.

Personalized Energy Insights: An AI agent that provides households with actionable recommendations to reduce waste, such as identifying energy-hungry appliances.
Dynamic Demand Response: Incentivizes consumers to shift usage to off-peak hours through automated smart-home integrations (e.g., charging an EV when wind power is plentiful).

Watch out this space, or talk to us on how we are developing in this domain.