Multi-Modal Occupancy Sensing for HVAC Airflow Control
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Solution Overview
Problem
HVAC systems face challenges in efficiently adjusting ventilation airflow based on occupancy levels, leading to energy inefficiencies and potential safety issues due to binary occupied/unoccupied decisions, which do not account for varying room sizes and occupancy patterns.
Innovation Solution
An occupancy sensing system that uses a multi-modal, multi-sensor design combining high-resolution panoramic cameras and low-resolution thermal sensors, with advanced fusion algorithms to estimate occupancy levels, allowing for fine-grained air volume control and minimizing sensor installation costs, while ensuring privacy and energy savings.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If binary occupied/unoccupied decisions are used for HVAC control, then system operation is simplified, but energy efficiency and safety are compromised due to inability to account for varying occupancy levels
Solution Approach 1:
The system transitions from binary occupancy detection to continuous occupancy estimation by changing the detection parameter from simple presence/absence to quantitative crowd density measurement. The crowd density sensor measures parameters such as thermal signatures, motion patterns, or electromagnetic field disturbances to estimate the number of occupants, enabling proportional HVAC adjustment rather than binary on/off control.
Solution Approach 2:
The patent replaces traditional mechanical or binary electronic occupancy sensors with advanced crowd density sensing technology. This substitution enables continuous analog measurement of occupancy levels rather than discrete binary states, allowing for fine-grained modulation of HVAC systems based on actual crowd density in real-time.
2Ease of manufacture
If traditional occupancy sensors are used, then installation is simple, but they fail to capture varying occupancy patterns and room size variations
Solution Approach 1:
The crowd density sensor is designed with multi-functionality to serve various occupancy detection needs across different room sizes and configurations. A single sensor type can detect both individual occupancy in small rooms and crowd density in large venues, adapting to different spatial scales and occupancy patterns without requiring different sensor models or complex installation configurations.
Solution Approach 2:
The system adds a new dimension of measurement by transitioning from binary occupancy detection to continuous crowd density measurement. This dimensional change enables the system to capture varying occupancy patterns and account for room size variations, providing rich analog data that reflects the actual occupancy state across different spatial scales.
3Measurement precision
If high-precision occupancy detection is implemented, then ventilation accuracy improves, but system cost and complexity increase
Solution Approach 1:
The system employs cost-effective crowd density sensor technologies that provide sufficient measurement precision without requiring expensive specialized equipment. By using readily available sensors capable of measuring thermal, electromagnetic, or acoustic signatures, the system achieves accurate occupancy detection while maintaining economical installation and operation.
Solution Approach 2:
The system uses intermediate measurement parameters such as thermal signatures, motion patterns, or electromagnetic field disturbances as mediators to infer occupancy levels. Rather than directly counting occupants, the sensors detect indirect physical phenomena that correlate with crowd density, simplifying the measurement process while maintaining detection accuracy.
4Loss of energy
If occupancy underestimation is allowed for energy savings, then energy consumption decreases, but safety and health requirements may be compromised
Solution Approach 1:
The system implements feedback mechanisms that continuously monitor occupancy levels and adjust HVAC operation accordingly. By providing real-time feedback on actual crowd density measurements, the system prevents excessive energy savings that could compromise safety, ensuring that ventilation requirements are met while optimizing energy consumption based on actual occupancy conditions.
Solution Approach 2:
The system dynamically adjusts HVAC operation based on real-time occupancy measurements rather than using fixed binary control settings. This dynamic adjustment allows the system to optimize energy savings during low-occupancy periods while automatically increasing ventilation capacity when occupancy levels rise, maintaining safety and health requirements throughout varying operational conditions.
Data Source
AI summary
Sensing and control apparatus for a building HVAC system includes interior and boundary sensors, such as cameras and thermal sensors, generating sensor signals conveying occupancy-related features for an area. A controller uses the sensor signals to produce an occupancy estimate and to generate equipment-control signals to cause the HVAC system to supply conditioned air to the area based on the occupancy estimate. The controller includes fusion systems collectively generating the occupancy estimate by corresponding fusion calculations, the fusion systems producing a boundary occupancy-count change based on sensor signals from the boundary sensors, an interior occupancy count based on sensor signals from the interior sensors, and the overall occupancy estimate. Fusion may be of one or multiple types including cross-modality fusion across different sensor types, within-modality fusion across different instances of same-type sensors, and cross-algorithm fusion using different algorithms for the same sensor(s).