Occupancy Tracking Using User Device Detection for HVAC Control
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Solution Overview
Problem
Existing HVAC systems cannot determine the number of people present in a space or identify individuals, limiting their ability to provide personalized temperature settings and efficient operation.
Innovation Solution
An occupancy tracking system that uses environmental information, such as audio signals, user device detection, and wireless signal strength, to predict the number of people in a space and adjust HVAC settings accordingly, incorporating machine learning models to filter out electronic device signals and identify personal user devices.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If proximity sensors or motion detection sensors are used to determine occupancy, then the system can detect whether a space is occupied, but it cannot determine the number of people present or identify individuals
Solution Approach 1:
The patent segments the occupancy detection problem into multiple independent detection channels: audio signal analysis for voice detection, wireless device detection for device presence, and machine learning models for synthesizing occupancy information. Each channel provides specific types of information that, when combined, resolve the contradiction between simple detection and detailed characterization.
Solution Approach 2:
The system uses multi-functional detection approaches where audio sensors serve both occupancy detection and individual identification purposes, wireless sensors detect both device presence and signal characteristics for occupancy inference, and machine learning models process multiple data types to provide comprehensive occupancy information including headcount and identity.
2Ease of operation
If existing HVAC systems use binary occupancy indication, then the system operation is simple, but the system cannot provide personalized HVAC settings or efficient management based on actual occupancy levels
Solution Approach 1:
The system transitions from static binary occupancy indication to dynamic multi-level occupancy characterization. The machine learning models continuously process audio and wireless sensor data to provide real-time occupancy levels, individual identities, and personal preferences, enabling the HVAC system to dynamically adjust settings based on actual occupancy conditions rather than simple occupied/unoccupied states.
Solution Approach 2:
The system implements feedback loops where occupancy detection information (number of people, identities, preferences) continuously informs HVAC system adjustments. The machine learning models analyze sensor data to infer occupancy characteristics and provide feedback signals that optimize HVAC operation, creating a closed-loop system that adapts to changing occupancy conditions.
Data Source
AI summary
An occupancy tracking device configured to identify devices connected to an access point over a predetermined time period. The device is further configured to populate entries in a device log for the identified devices. The device is further configured to determine a predicted occupancy level and to control a Heating, Ventilation, and Air Conditioning (HVAC) system based on the predicted occupancy level.