Technology

FSIGHT's technology is based on 3 pillars

Forecasting, Optimization & Trading

Each FSIGHT Energy A.I. agent optimizes DER energy flow to ensure maximal utilization of renewable energy at minimal costs and maximum efficiency.

Predict

Our field-proven machine learning engine provides an automatically calibrated forecasting model. We create bottom-up predictions (demand, production and pricing) on a minute by minute basis.

A unique data recovery system allows to support both smart meters and legacy meters, and big data infrastructure provides scalability for up to 1 sec data interval.

Optimize

A Heuristic tailor-made algorithm for optimal strategy search takes into account price fluctuations, weather conditions, and available consumption flexibility.


The system employs probabilistic techniques to handle different possible scenarios of nano-grid, micro-grid and VPP-level energy balance. The optimization engine supports multi goal optimization of cost, emissions, and convenience as well as planning tools for most efficient DER deployment.

Trade

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 maximize energy costs or Peer to Peer energy prices to maximise energy trading value.

Architecture

To achieve high performance, scalability, and resiliency of the system, Energy A.I. 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.
A unique data recovery system allows to support both smart meters and legacy meters, and big data infrastructure provides scalability for up to 1 sec data interval.

Forecasting

Field-proven machine learning and artificial intelligence engine provide an automatically tailored forecasting model for each building and asset to create bottom-up predictions on a minutely basis.

A unique data recovery system allows to support both smart meters and legacy meters, and Big data infrastructure provides scalability for up to 1 sec data interval.

Optimization

Heuristic tailor-made algorithm for optimal strategy search takes into account price fluctuations, weather conditions, and available consumption flexibility. The system employs probabilistic techniques to handle different possible scenarios of nano-grid, micro-grid and VPP-level energy balance.

The optimization engine supports multi goal optimization of cost, emissions, and convenience as well as planning tools for most efficient DER deployment.

Architecture

To achieve high performance, scalability, and resiliency of the system, Energy A.I. 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.