Thermostat Occupancy Schedule Correction for Energy-Saving HVAC Control
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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 predict user behavior and preferences by collecting data from user devices, interactions, and other devices, generating a predicted occupancy schedule to adjust set point temperatures for energy savings and reducing wear on HVAC components.
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 user comfort level may be affected when the user is actually present
Solution Approach 1:
The system performs preliminary actions by adjusting the set point temperature in advance based on predicted user absence. The machine learning model predicts when the user will be away, and the system proactively adjusts temperatures before the user leaves, thereby reducing energy consumption while being ready to revert if the prediction is incorrect.
Solution Approach 2:
The system implements feedback by comparing predicted occupancy status with actual user presence. When the user manually adjusts the temperature or interacts with the thermostat, the system detects this feedback and corrects its predictions, thereby maintaining user comfort while preserving energy savings from accurate predictions.
2Reliability
If the HVAC system requires user input to provide scheduling information, then user comfort is maintained, but the system cannot automatically adjust temperatures and energy saving benefits are lost
Solution Approach 1:
The system performs self-service by using the machine learning model to automatically generate occupancy schedules without requiring explicit user input. The system observes user behavior patterns independently and makes autonomous temperature adjustments, thereby achieving both user comfort (through accurate predictions) and energy savings (through automatic adjustments).
Solution Approach 2:
The system replaces the mechanical approach of requiring explicit user scheduling input with an intelligent machine learning-based prediction system. This substitution allows the system to automatically infer user schedules from behavioral data, eliminating the need for manual input while maintaining accuracy in temperature control.
3Measurement precision
If the system collects and processes user behavior data from multiple devices, then prediction accuracy is improved, but device complexity increases
Solution Approach 1:
The system applies universality by using a single machine learning model to process multiple types of data sources (location data, device interactions, network connections) through a unified prediction framework. This multi-functional approach improves prediction accuracy while avoiding the complexity of separate processing systems for each data type.
Data Source
AI summary
A heating, ventilation, and air conditioning (HVAC) control device is configured to record a plurality of actual occupancy statuses, to determine a plurality of corresponding predicted occupancy statuses, and to compare the plurality of predicted occupancy statuses to the plurality of actual occupancy statuses. The device is further configured to identify conflicting occupancy statuses based on the comparison. A conflicting occupancy status indicates a difference between an actual occupancy status and a corresponding predicted occupancy status. The device is further configured to identify timestamps corresponding with the conflicting occupancy statuses, to identify historical occupancy statuses corresponding with the identified timestamps, and to update the conflicting occupancy statuses in the predicted occupancy schedule with the historical occupancy statuses.