Before01
Company overview
- HVAC drives energy use
- Carbon data demanded
- Uneven manual control
AX Solution
Energy efficiency optimization
Energy-cost optimization realized through data-driven intelligent control
OVERVIEW
Not just measuring what energy is used —controlling it down to the least possible
Beyond watching consumption: the operating conditions of the equipment itself come into the AI’s control range.
KEY FEATURES
Four capabilities that optimise energy
From real-time monitoring through equipment efficiency and demand forecasting to carbon management.
Capability Index04
Energy Capability
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01
Real-time energy monitoring
Equipment efficiency optimization
Energy demand forecasting
Integrated carbon emissions management
It collects and visualizes the power consumption of key equipment and lines across the plant in real time. Energy consumption is grasped at a glance so waste is caught immediately, and peak-demand periods are managed to realize cost savings.
Management Scope
- Per-equipment power collection
- Real-time consumption board
- Peak demand control
- Abnormal consumption alerts
- Energy data retention
- Period-over-period analysis
Real-time energy monitoring
Tracks electricity, gas, steam and other energy use across the whole plant and its main equipment in real time
SYSTEM ARCHITECTURE
How it is built
Floor energy data collected and analysed, then returned to how the equipment runs.
Minimize unnecessary energy consumption and cut costs.
Power management
HVAC systems
Smart buildings
Renewable energy
Data centers
Lighting and utilities
Power management
HVAC systems
Smart buildings
Renewable energy
Data centers
Lighting and utilities
Energy is not used sparingly. It is used when it should be.
- Electricity and fuel use
- Equipment operating conditions
- Production plan
- Tariff and peak
- Renewable generation
AI Energy Optimization
- Per-machine energy intensity analysis
- Load and peak prediction
- Operating and switching scenario recommendation
- Energy intensity
- Peak demand
- Energy cost
- Renewable utilisation
EFFECT
Business impact
What it leaves behind — in energy intensity, demand peaks, energy cost and renewable share.
BUSINESS OUTCOME
Energy data,turned into measurable results
- UNIT
Lower energy intensity
Energy per unit produced is broken down by process to find the waste, and optimal set-points per machine cut standby loss.
Intensity · standby loss · set-points
- PEAK
Lower demand peaks
Overlapping loads are predicted and spread across the schedule, so the contracted demand limit is not crossed.
Peak demand · load spreading · contract limit
- COST
Lower energy cost
Peak charges and standby loss come down together, and tariff and run plan are matched so the same output costs less to power.
Electricity · gas · run plan
- RENW
Higher renewable share
Generation forecast and load plan are matched so self-generated power is used first, and the renewable share is tracked with the certificates that prove it.
Renewable share · self-consumption · certificates
CASE STUDY
Case study
Model predictive control cut HVAC energy by 15-30% while carbon data was managed alongside it.
Customer industry
High-layer-count PCB (MLB) manufacturing
After02
Implementation
- HVAC and climate data
- Predictive control
- PMV and VAV control
Business impact
Energy optimisation where AI runs the HVAC on its own
Energy is not about using less. It is about choosing when.
From a per-asset usage read to a control plan matched to your tariff — the saving in numbers first.
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