Self-Adjusting Floor Warming Thermostat Using Occupancy Learning
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Solution Overview
Problem
Standard setback thermostats for floor warming systems are complex to program, fail to accommodate diverse user lifestyles, and often lead to energy waste due to manual operation or inability to adjust to changing usage patterns.
Innovation Solution
A self-adjusting thermostat that senses occupancy patterns using electronic devices and processes this data with a microprocessor algorithm to adjust temperature settings accordingly, optimizing energy use based on expected usage.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If standard setback thermostats are used with fixed time periods and temperature settings, then the system provides basic temperature control capability, but the device fails to accommodate diverse user lifestyles and changing usage patterns
Solution Approach 1:
The thermostat automatically learns and adapts to user occupancy patterns without requiring manual programming. The system monitors occupancy over time and autonomously adjusts temperature settings based on learned patterns, eliminating the need for users to program complex schedules while maintaining high adaptability to diverse lifestyles
Solution Approach 2:
The thermostat transitions from static fixed time periods to dynamic adaptive time periods that automatically adjust based on learned occupancy patterns. The system continuously updates its understanding of user behavior and modifies temperature control schedules in real-time to match actual usage patterns
2Loss of energy
If manual operation of setback thermostats is used, then the device is easy to operate, but energy waste occurs due to inability to adjust to changing usage patterns
Solution Approach 1:
The thermostat implements continuous feedback loops where occupancy sensors provide real-time data about actual usage, the system processes this information through learning algorithms, and automatically adjusts temperature settings accordingly. This closed-loop feedback enables the system to minimize energy consumption by adapting to changing usage patterns without requiring manual user intervention
Solution Approach 2:
The patent replaces manual mechanical programming operations with electronic sensing and automated computational algorithms. Occupancy is detected through electronic sensors rather than manual input, and temperature control decisions are made through microprocessor-based learning algorithms rather than manual schedule programming
3Adaptability or versatility
If fixed time periods are used in setback thermostats, then the device structure is simple, but the system cannot recognize diverse lifestyle patterns
Solution Approach 1:
The thermostat performs preliminary learning during an initial period to establish baseline occupancy patterns before beginning automated temperature control. This preliminary action allows the system to pre-program adaptive schedules based on observed behavior, enabling accurate pattern recognition without requiring complex real-time decision-making algorithms
Data Source
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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.