A Digital Twin is a virtual representation of a physical system, process, or environment that is continuously updated with real-world data to enable monitoring, simulation, optimization, and forecasting. In energy systems, digital twins integrate data from IoT sensors, operational systems (e.g., SCADA, EMS), and external data (e.g., weather, market signals) to mirror and predict the performance of energy assets or networks in real time.

The real-world component represented by the digital twin—e.g., a solar
inverter, EV charging station, or oil pump.
The mathematical and simulation-based model mirroring the behavior of
the physical system.
Continuous or periodic updating of the twin with real-time data
from sensors and IoT gateways.
Use of AI or physics-based algorithms to forecast outcomes such as
power output or equipment failure.
Running 'what-if' analyses on operational or design scenarios to
test outcomes.
Integration of weather, production, and market data to predict
generation, consumption, and costs.
Identification of anomalies or performance
degradation before downtime occurs.
Maintenance activities recommended automatically by the
twin based on predicted failures.
Control feedback where the twin predicts performance and triggers
real-world control actions.
The continuous data lineage connecting the twin to all lifecycle systems
ensuring traceability.