By forecasting and analysing energy flows our technology enables utilities, smart home providers and distributed energy resource owners to optimise behind-the-meter energy generation, storage, usage and trading. Each Energy AI agent acts both independently and cooperatively to ensure maximum utilisation of the distributed energy resources.
To achieve high performance, scalability, and resiliency of the system, Energy AI is built in a hybrid Fog architecture. This approach uses a combination of centralized computing capability for forecasting grid and prosumer behavior together with decentralized and autonomous agents for local optimization.
The traditional centralized energy grid is evolving into a distributed grid. Affordable and renewable energy production, storage and IoT technologies are being introduced on a large scale . These new technologies are an opportunity to optimize our smart energy homes and micro-grids, while driving efficiency across the national grid.
We are transitioning to a sustainable energy society. This means that cooperation among all grid users will increase, thus maximising clean energy usage and minimizing waste. Communities of conscious energy users are growing. More and more consumers are becoming “Prosumers” - in that they don’t just consume but also produce and trade their energy. In the near future, energy production, storage, and usage will all be optimised and traded automatically amongst millions of people.
The evolution of a new electricity grid will enable us to seamlessly add renewable energy sources and intelligently manage production and storage. Ideally, this will result in a clean, resilient and more efficient energy grid. Millions of energy-related Artificial Intelligence agents will optimize our smart energy homes and appliances in order to maximize energy utilization and will allow us all to trade efficiently with our neighbors, communities, and the rest of the grid.
Collects data from various sources (e.g. smart meters, weather forecasts providers, special days calendars and more) extrapolates missing consumption data if needed, and forecasts consumption, renewable production, and energy prices for both traditional and distributed grids with full bottom-up architecture.
Optimizes the setup of Distributed Energy Resources (DER), and their real-time operations, based on various inputs such as prediction data, constraints, prosumers, and ecosystem objectives.
Automatically executes Peer-to-Grid and Peer-to-Peer trading decisions. Maximal energy revenues and minimal costs are achieved through an autonomous agent executing trading decisions based on the PREDICT and OPTIMIZE results.