Utilities, manufacturing, process plants
Energy Performance Insights
Use EAHM to explain where energy is being consumed, which assets or operating conditions are driving abnormal usage, and what teams should review first.
Business problem
Energy cost can increase across utilities, machines, and plant systems without a clear explanation. Teams may see higher bills or higher kWh numbers, but the root cause is often split across operating hours, load patterns, equipment condition, production demand, leakage, inefficient control, or abnormal events.
Where it applies
- Compressed air systems, pumps, chillers, HVAC, boilers, cooling towers, and large motors
- Plants with high utility cost and limited asset-level energy visibility
- Facilities that need energy reporting for operations, finance, or management review
How EAHM supports this use case
EAHM combines data, asset context, analytics, AI, dashboards, and reports so the use case can move from visibility to operational action.
Data ingestion
Connect meters, VFDs, PLCs, SCADA, historians, and energy management data so energy signals are tied to asset and process context.
Asset modeling
Group energy consumption by plant area, utility system, line, machine, asset class, or cost center for meaningful comparison.
Analytics
Compare consumption against runtime, load, production, operating state, and historical baseline to identify abnormal usage.
AI insights
Help users ask why consumption increased, which assets changed behavior, and what operating conditions contributed.
Dashboards and reports
Provide daily, weekly, asset-wise, and executive energy views with trends, summaries, and exception reports.
Useful signals
Customer value
- Identify assets or systems causing abnormal energy use
- Reduce manual energy review effort
- Support energy performance meetings with evidence
- Connect energy cost with operations and maintenance decisions
Practical starting scope
- 1Start with one high-consumption utility system
- 2Build asset-wise energy dashboards and baselines
- 3Add AI prompts and exception reports after data quality is stable