AI- driven
Prediction optimization
Replacing manual parameter adjustment with machine learning
Dynamic load
Real-time dispatch
Adjustments based on weather, occupancy rate, and electricity price.
Auditable carbon reduction
carbon footprint
Quantifying the effectiveness of reduction and the net zero path
Comfort + Energy Saving
Win-win balance
Energy conservation should not come at the expense of comfort.
Subdividing application scenarios
Commercial lobby / Owner
AI optimizes overall building energy consumption, reduces operating costs, and advances carbon reduction goals.
Data Center / High-Energy-Consuming Facilities
Predictive scheduling of cooling and power loads improves PUE.
Chain Stores / Multi-Branch EnterprisesUnified multi-site optimization and carbon reduction rankings to replicate best practices.
ESG
Support companies’ net-zero and sustainability disclosures with auditable carbon reduction data.
Traditional energy conservation relies on manual settings and fixed schedules, making it difficult to adapt to dynamic changes in weather, population flow, and electricity load. Even with the installation of monitoring and measurement systems, the vast amount of historical data is often only used for retrospective review and fails to translate into proactive and timely energy-saving and carbon-reduction decisions.
Air conditioning and lighting operate on a fixed schedule, failing to be dynamically optimized.
Historically, there is a vast amount of energy data, yet it is difficult to translate it into action.
Peak loads and energy waste are difficult to predict and avoid in advance.
The carbon emission reduction targets lack a quantifiable and sustainably optimized implementation mechanism.
FAQ
VITEG adds an AI analytics layer to iBSMon and energy metering, using machine learning to learn the building’s operating patterns. It combines variables such as weather, occupancy, and electricity prices to perform predictive control and load optimization, allowing air conditioning, lighting, and electromechanical equipment to maximize energy saving and carbon reduction while maintaining comfort.
Our Solution
Monitoring objects
Energy consumption of air conditioning unit and refrigeration system
Lighting circuit power consumption and occupancy
Electromechanical equipment load and operating mode
Indoor environment and comfort parameters
External weather and temperature forecast data
Carbon emission estimation and reduction effectiveness
End-to-end workflow
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Data aggregation
Integrate iBSMon device, energy metering, and environmental data to create a historical dataset that AI can learn from.
Model learning
Machine learning is used to build energy consumption and equipment behavior models to identify waste patterns and optimization potential.
Predictive Control
By combining forecasts of weather, pedestrian traffic, and electricity prices, the load on air conditioning and equipment can be adjusted in advance.
Automatic optimization
It automatically adjusts operating parameters within the comfort and safety threshold, continuously approaching optimal energy efficiency.
Carbon Optimization Report
Output auditable reports on energy savings and carbon emission reductions, supporting ESG and net-zero disclosure.
Included Capabilities / Solution Capabilities
AI Energy Optimization
Machine learning is used to continuously optimize the operating strategies of air conditioning, lighting, and electromechanical equipment.
Predictive control
Adjust equipment in advance based on predicted demand to avoid peak loads and energy waste.
Carbon emission visualization
Convert energy data into carbon emissions to quantify the effectiveness of carbon reduction.
Continuous improvement loop
The model is continuously retrained based on feedback from the results, forming a continuous cycle of self-optimization.
Deployment mode