Occupancy Tracking Using Device Detection for HVAC Control
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Solution Overview
Problem
Existing HVAC systems cannot accurately determine the number of people present in a space, limiting their ability to provide personalized temperature settings and efficient energy management.
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.
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 who is present
Solution Approach 1:
The patent segments the occupancy detection task into multiple independent detection methods: audio signal analysis for voice detection, wireless device detection for device presence, and environmental sensor data for contextual information. Each method contributes partial information that, when combined, provides comprehensive occupancy data including number of people and identity identification.
Solution Approach 2:
The patent merges multiple detection systems (audio sensors, wireless communication modules, environmental sensors) into a unified occupancy tracking system. By combining data from these diverse sources, the system overcomes the limitations of individual sensors and achieves both accurate occupancy counting and person identification capabilities.
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 energy management
Solution Approach 1:
The patent implements dynamic HVAC control that adapts to changing occupancy conditions in real-time. The system continuously monitors occupancy data from multiple sensors and dynamically adjusts HVAC settings based on the number of people present and their identified preferences, transitioning from static binary control to dynamic multi-level control.
Solution Approach 2:
The patent changes the control parameters from simple binary occupancy states to multi-dimensional parameters including occupancy count, person identity, and individual preferences. This enables personalized HVAC settings for each detected person while maintaining efficient energy management through data-driven control decisions.
3Measurement precision
If the system collects and processes multiple types of environmental information, then the occupancy prediction accuracy improves, but the system complexity increases
Solution Approach 1:
The patent implements a multi-functional processing module that handles multiple types of environmental information (audio signals, wireless device data, sensor readings) through a unified machine learning model. This universal approach allows the system to process diverse data types without requiring separate complex processing pipelines for each sensor type.
Solution Approach 2:
The patent introduces a machine learning model as an intermediary that bridges the gap between raw environmental data from multiple sensors and the final occupancy prediction. This intermediary layer automatically integrates and processes the diverse input data types, reducing the overall system complexity while maintaining high prediction accuracy.
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.