Programmable Thermostat Profiles for Occupancy-Based HVAC Control
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Solution Overview
Problem
Users often fail to optimize programmable thermostats effectively, leading to inefficient energy usage and increased costs due to difficulties in programming and predicting heating and cooling needs, resulting in unnecessary activation of HVAC systems when not home.
Innovation Solution
A system comprising a data acquisition and analysis subsystem that receives thermostat data to determine a cost-efficient management profile, predicting user behavior, and automatically adjusting the thermostat settings to reduce energy consumption, including features like occupancy detection and communication of optimized profiles to users.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If users program the thermostat manually, then the thermostat can adjust temperature according to programmed settings, but the programming process is difficult and burdensome leading to abandonment of programming function
Solution Approach 1:
The system performs self-learning by automatically observing user temperature adjustments and occupancy patterns over time, then generates optimized schedules without requiring manual programming. The thermostat serves itself by collecting data from sensors and user interactions to autonomously determine when heating or cooling is needed, eliminating the burden of programming while maintaining energy efficiency.
Solution Approach 2:
The system continuously monitors user manual adjustments to temperature settings and uses this feedback to refine and update the programmed schedule. By incorporating real-time feedback from occupancy sensors and user behavior patterns, the thermostat adapts its programming automatically, making the initial programming effort unnecessary while maintaining optimal performance.
2Reliability
If users program the thermostat to activate HVAC systems, then temperature control is achieved, but the systems may activate at times when the user is not home causing unnecessary energy consumption
Solution Approach 1:
The system uses occupancy sensors and learned user patterns to autonomously determine when the home is occupied or unoccupied, automatically adjusting HVAC operation accordingly. This self-service capability eliminates the need for users to manually program absence periods, ensuring reliable temperature control when needed while preventing energy waste during unoccupied periods.
Solution Approach 2:
The thermostat dynamically adjusts its operation schedule based on real-time occupancy detection and learned user behavior patterns rather than relying on static programmed schedules. This dynamic adaptation allows the system to maintain reliable temperature control when users are present while automatically reducing or eliminating HVAC operation when users are away, preventing energy waste.
3Ease of operation
If the thermostat uses simple programming, then ease of use improves, but the ability to predict user heating and cooling needs accurately decreases
Solution Approach 1:
The system performs self-learning by automatically observing and analyzing user temperature adjustments, occupancy patterns, and environmental conditions over time. This self-service data collection and analysis enables the thermostat to develop accurate predictions of user heating and cooling needs without requiring complex manual programming or user input, maintaining ease of use while improving prediction accuracy.
Solution Approach 2:
The system performs preliminary data collection and pattern recognition during an initial learning period, gathering information about user behavior and environmental conditions before full optimization is achieved. This preliminary action allows the thermostat to progressively improve its prediction accuracy over time while maintaining simple, user-friendly operation throughout the learning and optimization process.
Data Source
AI summary
Systems and methods for managing a programmable thermostat are described herein. One or more system embodiments include a programmable thermostat having a first management profile; a data acquisition subsystem; and a data analysis subsystem. The data acquisition subsystem is configured to receive thermostat data from the programmable thermostat, and the data analysis subsystem is configured to receive the thermostat data from the data acquisition subsystem, and determine a second management profile for the programmable thermostat based, at least in part, on the thermostat data.