Predictive temperature scheduling for a thermostat using machine learning
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Solution Overview
Problem
Existing HVAC systems lack the capability to determine whether a user is present or away without user input, leading to inefficiencies in energy consumption and wear on components, as they cannot automatically adjust set point temperatures without this information.
Innovation Solution
Employing machine learning to collect and analyze user behavior data from various devices, such as location, network connections, and interactions, to predict occupancy and adjust temperature settings for energy savings.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of energy
If the HVAC system automatically adjusts set point temperatures without user input, then energy consumption is reduced and component wear is minimized, but the system lacks the capability to determine user presence or absence accurately
Solution Approach 1:
The HVAC system uses machine learning models to autonomously determine user presence and adjust temperatures without requiring explicit user input. The system serves itself by collecting data from various sources, training models, making predictions about occupancy, and automatically implementing temperature adjustments based on these predictions.
Solution Approach 2:
The patent replaces manual user input mechanisms with automated machine learning-based detection systems. Instead of relying on users to manually provide presence information, the system uses computational models that process data from multiple sources (location services, network connections, device interactions) to automatically infer occupancy status.
2Measurement precision
If the HVAC system collects and processes user behavior data from multiple devices, then prediction accuracy improves, but system complexity increases
Solution Approach 1:
The HVAC system is designed to perform multiple functions: collecting data from various device types (location services, network connections, interaction logs), processing this diverse data through machine learning models, making occupancy predictions, and controlling temperature adjustments. This multi-functional approach consolidates what could be separate systems into a unified platform.
Solution Approach 2:
The patent combines multiple data collection mechanisms and processing functions into a single integrated system. Instead of having separate systems for location tracking, network monitoring, interaction logging, and temperature control, these functions are merged into one cohesive HVAC system that handles all tasks through unified machine learning models.
3Ease of operation
If the system makes conservative temperature adjustments, then user comfort is maintained, but energy savings are reduced
Solution Approach 1:
The system dynamically adjusts temperature set points based on predicted occupancy probability and user comfort preferences. Rather than using fixed conservative or aggressive adjustment rules, the system adapts its behavior in real-time based on machine learning predictions, allowing it to optimize between comfort and energy savings for each specific situation.
Solution Approach 2:
The patent changes the parameter of temperature adjustment aggressiveness based on occupancy prediction confidence and user preferences. The system can shift between conservative and aggressive adjustment strategies by modifying control parameters, enabling it to maximize energy savings when confidence is high while maintaining comfort when uncertainty exists.
Data Source
AI summary
A heating, ventilation, and air conditioning (HVAC) control device configured to receive a user input for controlling an HVAC system, to determine whether the user input indicates an energy saving occupancy setting, and to identify a first plurality of time entries that are associated with a confidence level for a predicted occupancy status that is less than a predetermined threshold value in the predicted occupancy schedule. The device is further configured to modify the predicted occupancy schedule by setting the first plurality of time entries to an away status when the user input indicates an aggressive energy saving occupancy setting. The device is further configured to modify the predicted occupancy schedule by setting the second plurality of time entries to a present status when the user input indicates a conservative energy saving occupancy setting. The device is further configured to output the modified predicted occupancy schedule.