MEET THE FUTURE OF GREEN ENERGY MANAGEMENT

Predict the future using the present. Take advantage of AI-driven, multi-variable models that consider cloud density at different elevations, humidity, temperature, wind, load demand, price, and device operational parameters, to predict and optimize operations and improve competitive position in capacity markets, to maximize profit and meet future energy demands accurately and dynamically, seconds, minutes, or days ahead.

Achieve performance that optimizes for environmental and strategic goals considering local demand, asset reliability, and resiliency objectives, by constantly synchronizing and optimizing each controlled asset with each other and with the network. Maximize your DER investments with value stacking, by combining use cases to unlock additional revenue streams.

Using digital twin modeling to facilitate continuous learning, refine the performance of your assets toward desired outcomes in real-time, while incorporating strategic goals, price, and compliance rules. As models constantly consume large amounts of real-time and historical data, they continue to adapt to changing conditions, learning from previous outcomes, resulting in more accurate and optimal decisions.

Assure safe and reliable operation of energy assets by enabling real-time, autonomous decision-making at the edge of the asset control system. Solving problems locally results in increased speed of action and higher level of situational awareness, which can lead to increased reliability, lower operational cost, and more effective asset performance monitoring.