HVAC Thermostat Machine Learning for Predictive Occupancy Scheduling
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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, generating a predicted occupancy schedule to adjust set point temperatures for energy savings and component protection.
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 user comfort may be compromised
Solution Approach 1:
The system performs preliminary actions by predicting user presence or absence in advance using machine learning models trained on historical data from user devices, interactions, and network connections. This allows the HVAC system to proactively adjust set point temperatures before the user actually arrives or leaves, thereby reducing energy consumption while maintaining comfort during occupied periods.
Solution Approach 2:
The system implements feedback mechanisms by continuously collecting data from user devices, thermostat interactions, and network connections to train and refine machine learning models. This feedback loop enables the system to learn user behavior patterns over time, improving prediction accuracy and ensuring that automatic temperature adjustments align with user comfort preferences.
2Measurement precision
If the HVAC system collects and processes user data from multiple sources, then prediction accuracy is improved, but system complexity increases
Solution Approach 1:
The system applies universality by using a single machine learning framework that processes multiple data sources (user device locations, travel directions, network connection types, thermostat interactions, and other device interactions) through unified algorithms. This multi-functional approach improves prediction accuracy while avoiding the need for separate specialized systems for each data type, thereby managing complexity.
3Extent of automation
If the HVAC system uses machine learning models to predict occupancy, then automatic temperature adjustment capability is enhanced, but computational resources and processing time increase
Solution Approach 1:
The system performs preliminary action by continuously training and updating machine learning models in the background using historical data, so that prediction models are ready for immediate use when occupancy prediction is needed. This pre-computation approach enables fast real-time predictions without requiring extensive processing during critical decision moments, thus maintaining high automation capability while minimizing processing delays.
Data Source
AI summary
A heating, ventilation, and air conditioning (HVAC) control device configured to generate the machine learning model using the first set of weights and the second set of weights. The machine learning model is configured to output a probability that a user is present at the space based on an input that identifies a day of the week and a time of a day. The device is further configured to determine a probability that a user is present at the space for a predicted occupancy schedule using the machine learning model, to determine an occupancy status based on a determined probability that a user is present at the space, and to set a predicted occupancy status in the predicted occupancy schedule based on a determined occupancy status for each time entry. The device is further configured to output the predicted occupancy schedule.