Cooling energy optimisation
Control cooling power to actual demand and save energy.
Cooling energy optimisation evaluates temperature, weather and operating data to run refrigeration only as needed, reducing over-cooling and peak loads.
- Cost reduction
- Better sustainability balance
- Higher quality
cooling energy costs
peak loads
payback
product quality
Calculated from saved electricity costs and reduced peak-load charges.
- 01
Data integration
Consolidate data from ERP, MES and further sources for Cooling energy optimisation.
- 02
Data quality
Clean, harmonise and validate data for plausibility.
- 03
Model development
Train and validate the Cooling energy optimisation model on historical data.
- 04
Pilot
Pilot Cooling energy optimisation in one area and collect feedback.
- 05
Rollout
Scale the solution and integrate it into operational processes.
Steps
Data sources
Stakeholders
From first data access to production – every step delivers a tangible interim result.
Sensor data
Real-time IoT and process sensors.
Weather data
Outside temperature, humidity and weather alerts.
Energy consumption
Electricity, gas, water and steam per area.
MES data
Production orders, feedback and machine data.
Inventory data
Stock levels, movements and locations.
Machine data
Condition, parameters and production counters.
Production manager
Uses insights directly in daily operations.
Sustainability lead
Captures and communicates sustainability metrics.
Maintenance
Plans maintenance and spare parts precisely.
Controlling
Quantifies effects and supports budgeting.
Cost reduction
Better sustainability balance
Higher quality
Higher efficiency
Lower risk
With a solid data foundation this use case gets faster, cheaper and far more stable.
Central data platform
Real-time data integration
Scalable analytics pipelines
Reusable data products
Energy optimisation
Process data enables demand-based energy control.
OEE monitoring
Availability data complements process metrics.
Predictive maintenance
Sensor data provides wear indicators.