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A Practical Multi-Sensor Cooling Demand Estimation Approach Based on Visual, Indoor and Outdoor Information Sensing
Junqi Wang1, Norman Chung Fai Tse2, Tin Yan Poon3
1Division of Building Science and Technology, City University of Hong Kong, Hong Kong, China. junqi.alan.wang@outlook.com.
Sensors (Basel, Switzerland)
|October 27, 2018
Summary
This study introduces a novel method for estimating cooling demand in buildings by detecting occupancy density instead of occupant count. This approach significantly improves accuracy and energy efficiency in heating, ventilation, and air conditioning (HVAC) systems.
Area of Science:
- Building energy management
- Artificial intelligence in HVAC systems
Background:
- Heating, Ventilation, and Air Conditioning (HVAC) system efficiency is crucial for building energy performance.
- Demand-based control strategies optimize HVAC services by accurately assessing cooling demand, which relies on occupancy detection.
Purpose of the Study:
- To propose a novel, low-cost, and privacy-friendly method for estimating cooling demand by detecting occupancy density.
- To improve the accuracy of cooling demand estimation in HVAC systems.
Main Methods:
- Occupancy density was estimated using image foreground moving pixels.
- An artificial neural network model was developed using occupancy density, indoor, and outdoor information for cooling demand estimation.
- Experiments were conducted in a university design studio.
Main Results:
- The proposed approach significantly reduced cooling demand estimation error by 67.4%.
- The R value for cooling demand estimation improved from 0.75 to 0.96.
- The method demonstrated computational efficiency and ease of implementation.
Conclusions:
- Detecting occupancy density is a more effective and efficient approach for cooling demand estimation compared to occupant count detection.
- The proposed method offers a practical solution for enhancing HVAC energy efficiency and can be integrated into existing building management systems.
Keywords:
HVACbackground subtractioncooling demand estimationoccupancy densityvision-based occupancy detectionMore Related Videos
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