Error correction for predictive schedules for a thermostat
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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, such as location, travel direction, network connections, and interactions with the thermostat, to generate a predicted occupancy schedule and adjust set point temperatures 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 decreased, but the user comfort level may be affected when the system makes incorrect adjustments
Solution Approach 1:
The system performs preliminary actions by collecting and analyzing user behavior data (location, travel direction, network connections, device interactions) before making temperature adjustments. This allows the HVAC system to predict user presence/absence and pre-adjust temperatures accordingly, reducing energy consumption while maintaining comfort through accurate predictions rather than reactive adjustments
Solution Approach 2:
The system implements feedback mechanisms by continuously monitoring user interactions with the thermostat and other devices, comparing predicted occupancy with actual occupancy, and using this information to refine future predictions. This feedback loop ensures that temperature adjustments are based on accurate, up-to-date user behavior patterns, maintaining both energy efficiency and user comfort
2Measurement precision
If the HVAC system collects and analyzes extensive user behavior data from multiple devices, then predictive accuracy is improved, but system complexity increases
Solution Approach 1:
The system applies universality by using a single HVAC controller to perform multiple functions: collecting data from various sources (thermostat interactions, user device locations, travel directions, network connections), analyzing this data to predict user behavior, and controlling temperature adjustments. This multi-functional approach improves predictive accuracy without requiring separate dedicated systems for each function, thereby limiting the increase in overall system complexity
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.