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 accordingly, enabling energy-saving adjustments while maintaining user comfort.
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 cannot accurately determine user presence or absence leading to potential comfort degradation
Solution Approach 1:
The HVAC system performs self-service by automatically collecting data from user devices and other smart devices in the environment, processing this data through machine learning models, and adjusting temperatures without requiring explicit user input. The system serves itself by inferring occupancy status from indirect signals like device locations, network connections, and interaction patterns.
Solution Approach 2:
The patent replaces manual user input mechanisms with automated electronic data collection and machine learning inference. Instead of relying on users to physically input their schedules or presence information, the system uses software-based machine learning models that process digital signals from various sources to automatically determine occupancy and adjust temperatures accordingly.
2Measurement precision
If the HVAC system collects and processes user behavior data from multiple devices, then prediction accuracy improves, but device complexity and data processing requirements increase
Solution Approach 1:
The HVAC system achieves multi-functionality by serving as both a temperature control device and a data collection hub. It aggregates information from multiple sources including user device locations, network connection types, and interactions with other smart devices, processing all this data through a unified machine learning model to generate occupancy predictions.
Solution Approach 2:
The patent introduces machine learning models as intermediary components that bridge the gap between raw data from multiple devices and the HVAC control decisions. These models process and interpret complex patterns from various data sources, transforming them into actionable occupancy predictions without requiring direct complex interactions between all system components.
3Extent of automation
If the system uses machine learning to predict user behavior, then automatic temperature adjustment capability is enabled, but the initial setup and training requirements increase complexity
Solution Approach 1:
The system performs preliminary actions by collecting and storing user behavior data during an initial training period before full automated control is activated. During this phase, the machine learning model learns patterns from actual user interactions and environmental data, building a foundation for accurate future predictions without requiring manual programming of user schedules or preferences.
Solution Approach 2:
The patent implements feedback mechanisms where the system continuously monitors actual user presence and temperature preferences, comparing these against machine learning predictions. This feedback loop allows the model to iteratively improve its accuracy by learning from discrepancies between predicted and actual behavior, gradually reducing the need for manual intervention while maintaining high automation levels.
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.