Intelligent Thermostat Schedule Learning From Manual Setpoint Resets
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing HVAC thermostats either lack user-friendly interfaces, leading to missed energy-saving opportunities, or overwhelm users with complex settings that are not optimally utilized, creating a tension between energy-saving sophistication and practical application in residential and commercial settings.
Innovation Solution
A thermostat with a ring-shaped user interface and processing system that automatically adjusts setpoint temperatures based on user input, generates schedules, and notifies users of energy-saving potential, featuring a simple and intuitive design to facilitate energy-efficient operation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of energy
If programmable thermostats with multiple settings are provided, then energy-saving capability is improved, but device complexity increases making the interface intimidating and difficult to use
Solution Approach 1:
The thermostat automatically learns and adapts to user temperature preferences and occupancy patterns without requiring manual programming. The system observes user manual adjustments and automatically generates optimized schedules, eliminating the need for users to navigate complex programming interfaces while still achieving energy-saving goals through automated behavior modeling
Solution Approach 2:
The system continuously monitors user manual temperature adjustments and occupancy patterns, using this feedback to automatically refine and update energy-saving schedules. This closed-loop learning process enables the thermostat to improve energy efficiency over time while maintaining operational simplicity for users
2Ease of operation
If simple non-programmable thermostats are used, then ease of operation is improved, but energy-saving opportunities are lost due to lack of automated control
Solution Approach 1:
The thermostat performs automated energy-saving functions independently by learning user patterns and generating schedules without requiring user programming input. This maintains the simplicity of a basic thermostat while incorporating the automated energy-saving capabilities of advanced programmable systems
Solution Approach 2:
The patent replaces manual programming mechanisms with automated electronic learning and adaptation systems. Instead of requiring users to mechanically program schedules through complex interfaces, the system uses electronic sensors and processors to automatically observe and learn user behavior patterns, then implements energy-saving control based on this learned information
3Ease of operation
If manufacturer default profiles are used, then ease of operation is maintained, but energy-saving effectiveness is reduced due to one-size-fits-all approach
Solution Approach 1:
The thermostat automatically adapts to each user's specific patterns and preferences through continuous observation and learning, eliminating the need for manual customization while achieving personalized energy-saving schedules tailored to individual household behaviors and requirements
Solution Approach 2:
The system transitions from static manufacturer default profiles to dynamic, continuously adapting schedules that evolve based on observed user behavior. The thermostat regularly updates its learned patterns and adjusts schedules in response to changing user preferences, occupancy patterns, and seasonal variations, maintaining both ease of use and adaptability
Data Source
AI summary
A user-friendly programmable thermostat is described that includes receiving an immediate-control input to change set point temperature, controlling temperature according to the set point temperature for a predetermined time interval, and then automatically resetting the set point temperature upon the ending of the predetermined time interval such that the user is urged to make further immediate-control inputs. A schedule for the programmable thermostat is automatically generated based on the immediate-control inputs. Methods are also described for receiving user input relating to the user's preference regarding automatically generating a schedule, and determining whether or not to automatically adopt an automatically generated schedule based on the received user input.