
Building energy and EUI analysis
Bring floor, zone, system, and meter data into one view to identify high-consumption areas, load composition, and operating-hour differences.

Operational Energy Intelligence for Buildings, Campuses, and Utilities
Unify BMS, meters, equipment status, spaces, and maintenance records in an operational digital twin for energy diagnosis, EUI and load analysis, Green Mark readiness, and corrective-action tracking.
Core building blocks that define how this page delivers operational value.
Connect BMS, meters, IoT, and historian data through Data Fusion Services, then place electricity, cooling, HVAC, lighting, compressed air, and utility consumption in spatial, system, and asset context.
Combine load, weather, occupancy, equipment status, and alarm history to identify abnormal consumption, drifting control strategies, underperforming assets, and cross-system issues.
When deeper analysis is needed, connect BIM/IFC, weather data, and operating records with EnergyPlus-based building energy models to compare control strategies, retrofit options, and operating setpoints.
Organize energy, environmental, inspection, maintenance, corrective-action, and work-order evidence for review. DataMesh supports preparation; it does not replace certification consultants or official assessment.
Turn AI findings, energy anomalies, and optimization recommendations into Inspector inspections, work orders, field execution, and verification records.
Practical applications and proven success scenarios across industries.

Bring floor, zone, system, and meter data into one view to identify high-consumption areas, load composition, and operating-hour differences.

Connect multi-vendor BMS, energy meters, and IoT data while keeping existing systems in place and creating one operational view for management and engineering teams.

Organize energy, environmental, inspection, maintenance, and corrective-action records by asset, space, and system relationships.

Analyze abnormal consumption and equipment efficiency across chillers, compressed air, power distribution, HVAC, and production utility systems.
Many energy programs stop at reporting. Teams can see consumption, but it is harder to explain which space, system, or asset caused an anomaly, and which team should act on it. DataMesh connects BMS, meters, IoT, BIM/IFC, maintenance records, and work orders into an operational digital twin so energy optimization moves from visibility to diagnosis, scenario validation, and corrective execution.
The solution supports commercial buildings, campuses, data centers, utilities, and industrial facilities. It helps with day-to-day energy governance while providing a clearer evidence base for programs such as Singapore BCA Green Mark readiness.
Green transformation starts with trusted operational data. Data Fusion Services connects multi-vendor BMS, energy meters, IoT sensors, historians, and business systems. FactVerse then maps that data to spaces, equipment, systems, and workflows.
In Inspector, Brick Schema support can provide an open asset ontology layer so building, equipment, point, and system relationships are described consistently. That makes each energy analysis or Green Mark readiness item easier to trace back to the relevant asset, data point, and operating record.
FactVerse AI Agent can help identify abnormal consumption, inefficient operating patterns, and control strategies that need review. When deeper analysis is required, DataMesh can connect BIM/IFC, weather data, and operating records with EnergyPlus-based building energy models to help evaluate EUI, load composition, and the potential impact of different actions.
This analysis can support:
BCA Green Mark 2021 emphasizes energy performance while also considering smartness, maintainability, whole-life carbon, health and wellbeing, and resilience. DataMesh does not provide automatic certification. Its role is to help teams organize distributed operating data, maintenance evidence, and corrective-action records into a reviewable operating evidence chain.
For Green Mark readiness, facility teams can continuously organize:
| Phase | Focus | Output |
|---|---|---|
| Data audit | Review BMS, meters, points, asset registry, and existing reports | Data scope and quality list |
| Baseline | Relate energy consumption to spaces, systems, assets, and operating hours | Energy baseline, priority systems, anomaly list |
| Analysis | Use AI analysis, rule review, and EnergyPlus modeling for scenario comparison | Recommendations, assumptions, priorities |
| Execution | Use Inspector for inspections, work orders, and corrective records | Traceable execution, verification, evidence chain |
Start with one building, a focused system group, or a campus sub-area. A pilot should validate data availability, anomaly accuracy, corrective-action workflow, and evidence quality. Energy impact depends on facility condition, control strategy, operating discipline, data quality, and execution depth, so it should not be promised as a fixed percentage.
A dashboard usually shows consumption and trends. DataMesh connects energy data to spaces, assets, system relationships, and work-order flows so teams can understand where a problem is, what may be causing it, and how to move it into a corrective-action loop.
No. Green Mark is assessed through BCA and related professional processes. DataMesh supports preparation by organizing operating data, maintenance evidence, corrective records, and traceable review materials.
Usually no. Data Fusion Services can connect existing BMS, meters, IoT, CMMS, and EAM systems through standard interfaces and protocols, then add a unified data and operational semantics layer above them.
EnergyPlus supports deeper building energy modeling and scenario comparison. Brick Schema helps organize buildings, floors, zones, equipment, meters, sensors, and control points as an open asset ontology so evidence and analysis can be traced to specific assets and data points.
There is no fixed percentage to promise. Outcomes depend on facility type, equipment condition, data quality, operating discipline, and corrective execution. Start with a focused pilot, establish the baseline, validate opportunities, then scale.