Digital Twins & Simulation
Asset lifecycle, scenario analysis, predictive analytics, AI
For: Asset Manager · Operations Engineer · Head of Engineering · Data Scientist
The challenge
Physical energy assets — solar arrays, battery systems, HVAC plants, industrial processes — degrade in ways that are invisible until failure. Operators rely on fixed maintenance schedules that either over-service healthy equipment or miss emerging faults. Performance simulations are run once during design and never updated with operational data. There is no continuous feedback loop between what was modeled and what is actually happening — meaning efficiency losses of 3-8% accumulate silently, and equipment replacements come as budget surprises rather than planned capital events.
Meridia modules that help
Facilities & Metering
Portfolio monitoring, meter hierarchies, EnPIs, power quality
Forecasting
Demand, generation, price, and weather forecasting
Renewable Assets
Solar siting, wind assessment, scenario economics
Battery Storage
Sizing, dispatch optimization, degradation, market ops
Demand Response & Flexibility
Load flexibility, demand response, virtual power plants
Energy Execution Intelligence
Process mining, dispatch conformance, prescriptive optimization
How it works
- 1
Build digital replicas of physical assets
Create physics-based models of your critical energy assets — solar PV arrays with cell-level degradation curves, battery systems with electrochemical aging models, HVAC equipment with thermodynamic performance maps. Calibrate each twin against commissioning data and manufacturer specifications.
- 2
Feed live operational data
Connect metered performance data — power output, temperatures, pressures, vibration — to continuously update each digital twin. Compare predicted vs. actual performance in real time to detect drift that signals degradation, fouling, or component failure before it impacts operations.
- 3
Run scenario simulations
Model what-if scenarios against your digital twins: how will a 10% load increase affect chiller efficiency? What is the optimal battery replacement year given current degradation rates? Should you repower a solar array or extend its operating life? Run Monte Carlo simulations to quantify uncertainty.
- 4
Deploy predictive analytics and AI
Train machine learning models on the gap between twin predictions and actual performance to detect anomaly patterns invisible to rule-based systems. Generate predictive maintenance schedules that minimize downtime cost while maximizing asset useful life — shifting from calendar-based to condition-based maintenance.