Occupancy-Learning Thermostat for Floor Warming Energy Control
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Solution Overview
Problem
Standard setback thermostats for floor warming systems are often complex to program, fail to accommodate diverse user lifestyles, and result in energy waste due to their inability to adjust temperature settings based on actual occupancy patterns, leading to increased energy usage and user frustration.
Innovation Solution
A self-adjusting thermostat that uses occupancy sensors to detect human presence and processes this data through an algorithm to adjust temperature settings dynamically, matching expected usage patterns and optimizing energy usage.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If standard setback thermostats use fixed time periods and default temperature settings, then the device complexity is reduced and ease of operation is improved, but the adaptability to diverse user lifestyles deteriorates and energy efficiency is reduced
Solution Approach 1:
The thermostat automatically learns and adapts to occupancy patterns without requiring user programming. The system monitors occupancy sensors over time, builds occupancy profiles autonomously, and adjusts temperature settings based on learned patterns, eliminating the need for manual configuration while maintaining high adaptability
Solution Approach 2:
The thermostat transitions from static fixed time periods to dynamic learned occupancy patterns. The system continuously updates occupancy profiles based on sensor data and adjusts temperature settings dynamically according to actual usage patterns rather than predetermined schedules
2Loss of energy
If standard setback thermostats deenergize heating during unoccupied periods, then energy efficiency is improved, but the loss of time for system restart increases when occupancy is detected
Solution Approach 1:
The thermostat performs preliminary heating before predicted occupancy periods based on learned patterns. By anticipating when occupants will return based on historical data, the system pre-heats the space, eliminating both energy waste from continuous heating and the time loss from restarting cold heating systems
Solution Approach 2:
The system continuously monitors occupancy sensor data and uses this feedback to refine occupancy profiles and adjust heating schedules. This closed-loop approach optimizes the balance between energy savings from reduced heating and maintaining comfort by predicting when heating should be restored
3Adaptability or versatility
If standard setback thermostats require manual programming of time and temperature settings, then the adaptability to user needs is improved, but the device complexity and ease of operation deteriorate
Solution Approach 1:
The thermostat performs self-programming by automatically learning occupancy patterns from sensor data over time. The system builds occupancy profiles, determines preferred temperature ranges, and creates heating schedules autonomously without requiring users to program time periods or temperature settings manually
Solution Approach 2:
The patent replaces manual mechanical programming interfaces with automated electronic learning algorithms. Instead of requiring users to set switches and dials, the system uses microprocessors to analyze occupancy sensor data and automatically generate control schedules
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
The self-adjusting thermostat significantly reduces energy consumption by accurately matching temperature settings to occupancy patterns, providing user convenience and improving energy efficiency by automatically adjusting heating based on actual usage without requiring manual programming changes.
Implementation Method 1
receiving a signal from an occupancy sensor in a first area indicating whether that area has been occupied
Implementation Method 2
send a signal to a heating device adapted for heating a second area to provide a temperature-related setting
Data Source
AI summary
A method of controlling the temperature of an environment, including the steps of providing a thermostat and receiving a signal from an occupancy sensor indicating whether an area has been occupied for each discrete time period of a 24 hour time period, assigning a first point value to each discrete time period where occupancy has been sensed, and repeating the steps for the next two 24 hour periods, and averaging the point values for each discrete time period in the first, second, and third 24 hour periods, to obtain an average point value, and sending a first signal to a heating device adapted for heating a second area to provide a temperature-related setting for a given discrete time period when the average point value for that discrete time period is above a threshold point value.