Real-time energy monitoring
Track consumption, power, demand and energy intensity live by facility, line, process and equipment.
AI-POWERED INDUSTRIAL ENERGY INTELLIGENCE
Beyond monitoring energy:
explain what drives it.
STR Energy Intelligence Platform connects directly to field equipment, analyzes real-time energy and production data, forecasts what comes next and explains the gap between expected performance and actual results.
Field connectivity
RS485 · Modbus RTU/TCP · Energy analyzers
Decision layer
AI forecast · Root cause · Cost · Carbon
FROM FIELD TO DECISION
One data chain, one source of truth
Signals from existing energy analyzers and industrial systems are securely processed with their context intact and turned into action.
01
Field equipment
Energy analyzers, meters, PLCs and production signals
02
RS485 / Modbus
Modbus RTU/TCP and verified register maps
03
Edge data layer
Timestamps, quality checks and secure transfer
04
Energy intelligence
AI models, digital twins and operational context
05
Decision and reporting
Alerts, action, cost, carbon and ISO 50001
PLATFORM CAPABILITIES
The full energy performance cycle in one product
AI anomaly detection
Detect deviations from normal operating profiles early and receive impact-prioritized alerts.
Energy consumption forecasting
Forecast future consumption using production plans, shifts, weather and process variables.
Cost and carbon impact
See the tariff-based cost and emissions impact of every deviation together.
ISO 50001 reporting
Build reporting workflows for energy baselines, EnPIs, objectives and improvement evidence.
Digital twin and equipment analytics
Model expected equipment behavior and evaluate efficiency, load and process relationships in context.
FORECAST · COMPARE · EXPLAIN
Not only how large the gap is — why it exists.
The platform calculates normally achievable production and expected energy consumption from the production plan and process conditions. It measures the gap against actual results and ranks likely causes with supporting evidence.
Every explanation remains traceable to the relevant signal, time window, baseline and confidence level.
ILLUSTRATIVE ANALYSIS VIEW
Line 02 · Last shift
AI baseline active
Expected production
1,240
unit
Actual production
1,108
unit
Production gap
−10.6%
−132 unit
Expected specific energy
0.82 kWh/unit
Actual specific energy
0.94 kWh/unit
+14.6%
Additional energy impact
+133 kWh
+₺ impact
Leading factors that explain the gap
Unplanned stop and restart38%
Line-state signals and the production counter diverge in the same time window.
Compressor efficiency loss27%
Power use at comparable load is above the 30-day baseline.
Reduced line speed21%
Cycle time increases while auxiliary loads remain constant.
Remaining unexplained gap14%
Operator notes or additional process data are required for validation.
Values shown are illustrative of the product approach; production models are calibrated with facility data.
OPERATING MODEL
Measure, learn, explain, improve
01
Connect
Discover devices, register maps, production counters and process signals.
02
Learn the baseline
Model normal energy and production behavior by equipment, shift and product context.
03
Forecast and compare
Generate expected values and calculate actual performance gaps continuously.
04
Explain the cause
Rank downtime, base-load, inefficiency and process factors with supporting evidence.
05
Verify impact
Track how each action changes energy, cost, production and carbon outcomes.
ONE SHARED VIEW
Energy, production and sustainability teams work from the same evidence
- Consumption, EnPIs and ISO 50001 evidence for energy managers
- Expected-versus-actual performance and loss drivers for production teams
- Equipment-level anomaly and inefficiency signals for maintenance teams
- Cost and carbon impact for finance and sustainability teams
Let’s find the hidden energy and production gap in your facility.
We can review your existing analyzers, Modbus network and most critical production line to define a focused pilot.