Method and apparatus for controlling energy in HVAC system
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Solution Overview
Problem
Current energy control systems in HVAC systems, such as those used in buildings, face inefficiencies due to static or dynamic setback controls that do not accurately account for user occupancy patterns, leading to increased energy consumption and discomfort upon the user's return, especially when using motion sensors or door locks alone.
Innovation Solution
A method and apparatus that utilize network-connected devices and sensors to determine user absence and adjust energy control based on departure and arrival probabilities, calculating a target temperature and setting reservations for energy control, considering user characteristics, environmental data, and HVAC system specifics to optimize energy usage and comfort.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of energy
If static setback control is used to save energy during user absence, then energy consumption is reduced, but user comfort deteriorates when the user returns quickly because the temperature cannot be restored in time
Solution Approach 1:
The patent applies dynamics by transitioning from static setback control to dynamic predictive control. The system continuously learns user arrival patterns and adjusts temperature control strategies in real-time based on predicted user return times. This allows the HVAC system to dynamically restore temperature before the user returns, maintaining both energy efficiency and user comfort.
Solution Approach 2:
The patent implements preliminary action by predicting user arrival times in advance and pre-restoring the temperature to comfortable levels before the user actually returns. The system uses historical data and machine learning to forecast when the user will return, then proactively adjusts the HVAC system to ensure comfort is ready, rather than waiting for the user to return and then reacting.
2Reliability
If setback control is delayed to wait for a predetermined absence duration, then false triggers are reduced, but additional energy consumption occurs during the waiting period
Solution Approach 1:
The patent applies feedback by continuously monitoring multiple occupancy indicators (motion sensors, door locks, appliance usage) and using this feedback to refine occupancy detection accuracy. The system learns from patterns in the data to distinguish between genuine absences and temporary situations, reducing false triggers while minimizing unnecessary energy consumption during detection periods.
Solution Approach 2:
The patent implements universality by using a multi-functional occupancy detection system that combines multiple sensors and data sources (motion sensors, door locks, appliance usage patterns). This multi-functional approach improves detection reliability by cross-validating signals from different sources, reducing false positives while enabling faster, more accurate occupancy status determination.
3Device complexity
If motion sensors or door locks alone are used for occupancy detection, then system complexity is reduced, but detection accuracy deteriorates due to blind spots or multiple users
Solution Approach 1:
The patent applies merging by combining multiple occupancy detection methods (motion sensors, door lock status, appliance usage patterns) into a unified predictive occupancy system. Rather than relying on a single sensor type, the system integrates data from multiple sources to cross-validate occupancy status, thereby improving detection accuracy while managing system complexity through centralized processing and machine learning algorithms.
Data Source
AI summary
The present disclosure relates to a sensor network, Machine Type Communication (MTC), Machine-to-Machine (M2M) communication, and technology for Internet of Things (IoT). The present disclosure may be applied to intelligent services based on the above technologies, such as smart home, smart building, smart city, smart car, connected car, health care, digital education, smart retail, security and safety services. method for controlling energy in a Heating, Ventilation, and Air Conditioning (HVAC) system includes determining whether a user is absent in a use space for energy control; if the user is absent, determining whether to initiate energy control by using a user's departure and arrival probability and a probability distribution of a length of time away, which are determined based on previously stored data.