Phase 1: Data Acquisition & Infrastructure

Deploy IoT Sensor Network Smart Meters, CTs, PTs, HVAC Sensors

Install Edge Gateways For local data aggregation &amp; preprocessing

Secure Data Transmission Via MQTT/OPC UA to cloud/on-prem server

Feedback Loop: Data
stream updates,
validating actions &
refining the twin

Data Ingestion & Processing
Time-series database,
stream processing

Phase 2: Digital Twin Core

Physics-Based Models
Building HVAC, solar
generation, battery
dynamics

Data-Driven Models ML forecasts for load, energy prices, weather

3D Spatial Model
Visualization of energy
flow & assets

Synchronization Engine
Bidirectional real-time
sync with physical system

Phase 3: Analytics & Live Digital Twin Virtual replica of energy system

Real-Time Monitoring &
Dashboard
KPIs: Power Quality,
Demand, Cost, Carbon
Footprint

Predictive Analytics
Anomaly detection, fault
prediction, asset health

Prescriptive Optimization
AI/ML recommends
optimal setpoints & actions

Phase 4: Automated Execution & Control

Execute via Control Actions
Adjust setpoints, shift
loads, dispatch storage

Notify Operator & Suggest
Actions
via HMI Dashboard or Alerts

Commands sent to
actuators,
BMS, Battery inverters

Phase 5: Continuous Learning & Refinement

Performance Analysis
Compare actual vs.
predicted savings &
behavior

Model Update Loop
Machine learning models
retrained on new data

Final Outcome:
Autonomous, Adaptive,
and Continuously
Improving Energy System

Proposal: Smart Energy
Management System (SEMS)
Using a Real-Time Digital Twin