Proximity-Based Thermostat Control for Real Occupancy Patterns
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Solution Overview
Problem
Conventional thermostats require users to manually program temperature settings, which can lead to inefficiencies due to user apprehension and mismatched predicted unoccupied periods, resulting in suboptimal energy savings.
Innovation Solution
A system utilizing a proximity detection module that communicates with a mobile device to adjust temperature settings based on the device's location, enabling remote control of HVAC systems by detecting proximity zones and altering settings automatically.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Extent of automation
If a thermostat requires manual programming of temperature settings for unoccupied periods, then the thermostat can provide automated temperature control, but the complexity of programming and user apprehension reduces actual usage and energy savings
Solution Approach 1:
The system uses the user's own mobile device location data to automatically determine occupancy status and adjust temperature settings without requiring manual programming. The thermostat serves itself by autonomously interpreting location information and making control decisions based on pre-configured occupancy zones.
Solution Approach 2:
The system introduces mobile device location data as an intermediary between the user and the thermostat control system. Instead of directly programming temperature schedules, the user's location information mediates the control decisions, simplifying the interaction while maintaining automated functionality.
2Loss of energy
If a thermostat uses predicted unoccupied periods for temperature adjustment, then automated energy savings can be achieved, but the predicted periods may not align with actual occupancy patterns
Solution Approach 1:
The system continuously receives real-time location data from mobile devices and uses this feedback to dynamically adjust temperature settings. This closed-loop approach ensures that temperature control decisions are based on actual occupancy status rather than static predictions, improving both energy savings and reliability.
Solution Approach 2:
The system transitions from static, pre-programmed temperature schedules to dynamic, real-time temperature adjustment based on current location data. The occupancy status and corresponding temperature settings change dynamically as users move in and out of defined zones, ensuring accurate alignment with actual occupancy patterns.
3Measurement precision
If a system uses multiple occupancy zones at different distances from the site, then more precise temperature control can be achieved, but the system complexity increases
Solution Approach 1:
The system segments the surrounding area into multiple occupancy zones (first zone, second zone, third zone) at different distances from the site, each with its own temperature adjustment parameters. This segmentation enables precise temperature control based on how close users are to the site while maintaining manageable system complexity through standardized zone definitions.
Data Source
AI summary
A system is provided for remote control of at least one operating condition of a site. The system includes a proximity detection module at a residential site, which is configured to receive a communication regarding a location of a location reporting device or a mobile device. The proximity detection module is configured to alter or adjust a current temperature setting for the site based on a distance or location of the location reporting device or mobile device relative to the site.