Each FSIGHT Energy AI agent optimizes the energy flow of Distributed Energy Resources (DER), such as smart houses or storage devices, to ensure maximal utilization of renewable energy at minimal cost and maximal efficiency.
Field-proven machine learning and artificial intelligence capabilities provide an automatically tailored prediction model for each building and asset to create bottom-up predictions on a per-minute basis.
A unique data recovery system supports both smart meters and legacy meters, and Big Data infrastructure scales up to a 1-second data logging interval.
Heuristic tailor-made algorithm for optimal strategy search takes into account price fluctuations, weather conditions, and available consumption flexibility. The system employs statistical techniques to handle different possible scenarios of nano-grid, micro-grid and Virtual Power Plant (VPP) level energy balance.
The optimization engine supports multi-goal optimization of cost, emissions, and convenience as well as planning tools for the most efficient DER deployment.
An autonomous trading layer allows buying and selling behind-the-meter energy at optimal prices through traditional Peer-to-Grid trading or Blockchain-based Peer-to-Peer trading.
The system integrates local retail time-of-use tariffs to minimize energy costs or Peer-to-Peer energy prices to maximize energy trading value.
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.