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 predict user behavior and preferences by collecting data from user devices, interactions, and network connections, allowing the HVAC system to generate a predicted occupancy schedule and adjust 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 minimized, but the system lacks accurate information about user presence or absence to make appropriate adjustments
Solution Approach 1:
The HVAC system uses machine learning models to autonomously determine user presence and adjust temperatures without requiring direct user input. The system serves itself by learning from historical data and making intelligent decisions about when to adjust set point temperatures for energy savings.
Solution Approach 2:
The system continuously learns from user behavior patterns and feedback data to improve its predictions of user presence. By analyzing historical occupancy data and adjusting its models over time, the system refines its ability to accurately predict when users are present or away, enabling more effective automatic temperature adjustments.
2Productivity
If the HVAC system collects and processes user behavior data through machine learning, then prediction accuracy improves and energy savings increase, but the system complexity increases
Solution Approach 1:
The system performs preliminary data collection and model training during off-peak times or in advance, building up historical occupancy data and trained machine learning models before they are needed for active temperature control. This allows the complex processing to be done beforehand rather than in real-time during operation.
Solution Approach 2:
The machine learning model acts as an intermediary between raw user behavior data and HVAC control decisions. The model processes and interprets complex patterns in the data, translating them into actionable predictions about user presence that the HVAC system can use for automatic control without needing to directly analyze the raw data complexity.
3Loss of energy
If the HVAC system makes temperature changes based on predicted user absence, then energy consumption decreases, but user comfort may be affected if predictions are inaccurate
Solution Approach 1:
The system applies partial temperature adjustments rather than extreme changes, especially when prediction confidence is moderate. This conservative approach ensures that even if predictions are not perfectly accurate, the temperature changes remain within comfortable ranges for users, balancing energy savings with comfort reliability.
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.