Enhanced automated control scheduling
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Solution Overview
Problem
Existing smart device control systems, such as thermostats, often rely on rules-and-exceptions-based learning approaches that can produce inefficient or erratic schedules due to overemphasis on initial interactions, leading to unsuitable temperature settings that fail to accurately adapt to user preferences.
Innovation Solution
Implementing a preference function that maps relative weights to device settings based on user behavior, allowing for the generation and optimization of automated schedules that better fit user preferences over time, incorporating factors like satisfaction, dissatisfaction, and behavioral patterns.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If a rules-and-exceptions-based learning approach is used to generate automated schedules, then the device can learn from user interactions, but it produces errant results and inefficient schedules due to overemphasis on initial interactions
Solution Approach 1:
The system performs preliminary actions by pre-heating or pre-cooling spaces before occupants arrive, based on learned schedules. This allows the system to proactively prepare environments rather than reactively responding to temperature adjustments, improving both reliability and energy efficiency
Solution Approach 2:
The system incorporates feedback mechanisms where user manual adjustments to temperature are used to refine and update the learned schedules. This continuous feedback loop allows the system to correct errant results and improve schedule accuracy over time, resolving the reliability issue while maintaining adaptability
2Loss of energy
If the system generates energy-efficient schedules, then energy consumption is reduced, but user comfort may be compromised by forcing efficient settings on users
Solution Approach 1:
The system dynamically adjusts temperature setpoints based on the interplay between energy efficiency goals and user comfort preferences. Rather than forcing fixed efficient settings, the system adapts schedules to accommodate user needs while minimizing energy consumption, achieving both objectives simultaneously
Solution Approach 2:
The learned schedule acts as an intermediary between energy efficiency requirements and user comfort preferences. It translates user behavior patterns into optimized temperature profiles that balance energy savings with comfort, preventing the need to force inefficient settings on users
3Adaptability or versatility
If the system frequently changes temperature setpoints to adapt to user preferences, then user comfort is improved, but system stability and predictability decrease
Solution Approach 1:
The system implements periodic temperature changes based on learned schedules that reflect typical user patterns. Rather than making frequent reactive adjustments, the system establishes stable periodic routines that adapt to user preferences while maintaining predictability and system stability
Data Source
AI summary
In an embodiment, an electronic device may include storage containing processor-executable instructions, a preference function that maps weights indicating likely user preferences for the range of values of a device setting in relation to a range of values of a variable, and a current automated device control schedule configured to control the device setting of the electronic device in relation to the variable, and a processor. The instructions may cause the processor to determine the current automated device control schedule based on the preference function by detecting user behavior that indicates satisfaction or dissatisfaction with values of the device setting in relation to the variable, updating the preference function based on the detected user behavior, and determining the current automated device control schedule by comparing a number of candidate device control schedules against the weights of the preference function and selecting the candidate with the highest score.