Adaptive Thermostat Programming from Manual Setpoint Overrides
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Solution Overview
Problem
Programmable thermostats often fail to accurately match user preferences due to mismatched schedules and temperature settings, leading to discomfort and excessive energy usage, as users struggle with manual overrides and reprogramming, especially when their schedules change.
Innovation Solution
A networked system that includes a thermostat connected to a server and website, allowing bi-directional communication to dynamically adjust temperature settings based on user inputs, manual overrides, weather conditions, and home thermal characteristics, using algorithms to predict temperature changes and adjust HVAC operation for optimal comfort and energy savings.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Use of energy by moving object
If a programmable thermostat is used to automate temperature adjustments, then energy consumption is reduced, but the system requires precise matching of programmed schedules with actual user behavior, which is difficult to maintain
Solution Approach 1:
The thermostat system automatically detects manual temperature adjustments made by users and uses this information to self-update its programming. The system monitors thermostat adjustments, identifies patterns in user behavior, and autonomously modifies scheduled temperature profiles to match actual preferences, eliminating the need for users to manually reprogram when schedules change.
Solution Approach 2:
The system implements a feedback loop where manual thermostat adjustments are detected and fed back into the programming algorithm. When users manually change the temperature, the system records these adjustments, analyzes them against the programmed schedule, and uses this feedback to refine future temperature profiles, creating a continuous learning cycle that adapts to user behavior.
2Ease of operation
If users manually override thermostat settings to correct temperature mismatches, then comfort is improved, but energy consumption increases due to excessive heating and cooling
Solution Approach 1:
The system monitors manual temperature adjustments and uses this feedback to learn user preferences. By detecting patterns in manual overrides, the system automatically modifies its programming to anticipate user needs, reducing the frequency and magnitude of manual adjustments required while maintaining comfort.
Solution Approach 2:
The thermostat system autonomously adjusts its programming based on detected manual overrides, eliminating the need for users to continuously correct the system. The learned preferences are automatically incorporated into future temperature profiles, allowing the system to serve itself by improving its own performance.
3Adaptability or versatility
If reprogramming the thermostat is done to match changed schedules, then comfort and energy efficiency are improved, but the process requires considerable effort and time
Solution Approach 1:
The thermostat system performs automatic reprogramming by detecting manual temperature adjustments and using this information to self-update its schedule. This eliminates the need for users to spend time manually reprogramming the thermostat when their schedules change, as the system autonomously adapts to new patterns.
Solution Approach 2:
The system uses feedback from manual temperature adjustments to automatically refine its programming. By continuously monitoring and learning from user behavior, the thermostat self-corrects schedule mismatches without requiring user intervention or time investment for reprogramming.
4Ease of operation
If manual temperature adjustments are made to correct thermostat mistakes, then user preferences are better met, but the adjustments often overshoot the desired temperature leading to excessive energy use
Solution Approach 1:
The system learns from manual temperature adjustments by analyzing the magnitude and direction of overrides. This feedback enables the system to better calibrate future automated adjustments, reducing overshooting behavior and achieving more precise temperature control that matches user preferences.
Solution Approach 2:
The thermostat autonomously learns optimal temperature settings from detected manual adjustments and applies this knowledge to self-correct its programming. The system eliminates the need for manual overrides by automatically achieving accurate temperature control through learned preferences.
Data Source
AI summary
Systems and methods are disclosed for incorporating manual changes to the setpoint for a thermostatic controller into long-term programming of the thermostatic controller. For example, one or more of the exemplary systems compares the actual setpoint at a given time for the thermostatic controller to an expected setpoint for the thermostatic controller in light of the scheduled programming. A determination is then made as to whether the actual setpoint and the expected setpoint are the same or different. Furthermore, a manual change to the actual setpoint for the thermostatic controller is compared to previously recorded setpoint data for the thermostatic controller. At least one rule is then applied for the interpretation of the manual change in light of the previously recorded setpoint data.