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 away status
Solution Approach 1:
The patent introduces machine learning models as an intermediary between the HVAC system and user presence detection. The ML models process data from multiple sources (geofencing, network activity, device interactions) to infer occupancy status, enabling automatic temperature adjustment without requiring direct user input or complex sensor arrays.
Solution Approach 2:
The system performs self-service by automatically learning user behavior patterns and making intelligent decisions about temperature adjustment. The ML models continuously improve their predictions by analyzing user interactions with the thermostat and other devices, allowing the system to autonomously optimize energy consumption based on inferred occupancy.
2Measurement precision
If the HVAC system collects and analyzes user behavior data from multiple devices, then occupancy prediction accuracy is improved, but data collection and processing complexity increases
Solution Approach 1:
The patent leverages existing multi-functional devices (smartphones, tablets, computers) that users already interact with throughout the day. These devices serve multiple purposes: they are not only communication tools but also occupancy sensors, providing location data, network connection information, and interaction patterns that the HVAC system uses to predict occupancy without requiring dedicated sensing hardware.
Solution Approach 2:
The system implements feedback loops where user interactions with the thermostat and other smart devices continuously refine the machine learning models. The models learn from actual occupancy patterns and adjust their predictions accordingly, improving accuracy over time while adapting to changing user behaviors and routines.
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.