Air-Conditioning System Optimization through video analytics

Project understanding

(i) To adopt the video analytics (people counting) technology with CCTV system in a dynamic visitor

flow venue (e.g. exhibition hall, museum, etc.) By capturing the real-time number of visitors at each

thematic area and using the data to optimize the Air-Conditioning System Control Algorithm (i.e.

adjust the air flow rate according to changes in comfortable environment to the public.)

(ii) To provide real-time dashboard to display the level of crowdedness of each thematic area to notify

the public instantly.

Aim

Energy Efficient Thermal Comfort

Concern• Response time of the HVAC system – the system unable to chase the new set point every second.• Depends on the condition of site, people counting through video / CCTV may have limitation and

unable to collect the data at once, this may cause “minority represent majority”.

Approach - Cooling load prediction

Fresh air provision

People heat

generation

Cooling load

Create predictive occupancy profile and cooling.

• Use past data (if any)• Upgrade existing CCTV to achieve people counting• Integrate with event schedule• Cross reference with chill water return temperature / cooling

load profile / CO2 level

Occupancy Load:

Approach - Heat Map

• Real-time display the level of crowdedness of each event area / zone• Air balancing at difference zone to optimize the fan power

Approach - HVAC system optimisation

Develop operation optimization through Machine Learning

Visualize control and operation strategy with dashboard

7 layers-Neural Network

No. of Equipment

Performance curve

Outdoor air conditions

Indoor air conditions

Cooling Load

Operation Schedule

Chiller Plant Operation Optimisation

Energy Consumption /Sensors data

• Adjust HVAC operation through predicted occupancyprofile.

• Operate HVAC system in best COP condition.

Approach - Dynamic retro-commissioning

• Map real time operation data onto theory performance curve, which be able to identify the performancegap and conduct dynamic retro-commissioning.

Approach - Indoor Air Quality enhancement

IAQ SENSORS

NEURON AIR PURIFIER

• Monitor real time indoor air quality and treatharmful pollutants dynamically.

• Enhance indoor environment and userexperience.

Summary

1. Construct video analytics technology based on the existing CCTV system installed for people counting andexplore potential mechanic learning algorithm for thermal comfort related set point prediction.

2. Occupancy profile refinery by CO2 level / chilled water return temperature.

3. Construct data driven occupancy profile / load data.

4. Develop AI model for chiller plant / air side unit optimization based on internal / external factors, cooling loadforecast and COP curve

5. Data analysis and “heat mapping” for the air distribution control / air balancing at difference zone to optimizethe fan power

6. Predictive maintenance for critical equipment with most power consumption. Identify potential to achieveenergy saving by optimizing the equipment maintenance schedules.

7. Indoor air quality analysis with using IoT Lora sensors to identify the potential improvement on fresh air intakescheduling.