Occupancy-Based HVAC Setback Control for Energy and Comfort
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Solution Overview
Problem
Current HVAC systems face inefficiencies in energy saving due to inaccurate occupancy detection and static setback control methods, which lead to discomfort and increased energy consumption, especially in diverse user demands and environments like hotels.
Innovation Solution
A method and apparatus that utilize network-connected devices and sensors to determine user occupancy or non-occupancy, analyze non-occupancy patterns, and perform dynamic temperature control based on probability distributions and user feedback to optimize energy saving while ensuring user comfort.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of energy
If static setback control using constant value is implemented, then energy saving is achieved during non-occupancy periods, but user comfort deteriorates when users return due to long temperature recovery time
Solution Approach 1:
The patent implements dynamic setback control by adjusting temperature setpoints based on predicted occupancy duration. The system calculates optimal temperature adjustments considering the predicted length of absence, HVAC system response characteristics, and building thermal mass, thereby optimizing both energy savings and comfort recovery time for each specific scenario
Solution Approach 2:
The system performs preliminary temperature adjustments before the user actually returns by predicting occupancy based on historical patterns and triggering pre-heating or pre-cooling sequences. This advance action ensures the building reaches comfortable temperatures by the time the user returns, eliminating the discomfort associated with static setback control
2Loss of energy
If setback control is initiated immediately when non-occupancy is detected, then energy saving is maximized, but energy consumption increases if the user returns quickly due to instantaneous temperature control requirements
Solution Approach 1:
The system performs preliminary occupancy duration assessment before initiating setback control. By predicting whether the absence will be brief or extended based on historical data and current context, the system only triggers temperature adjustments when the predicted absence duration justifies the energy investment, avoiding unnecessary cooling/heating cycles for short absences
Solution Approach 2:
The patent dynamically adjusts the setback control activation threshold based on multiple parameters including predicted occupancy duration, outdoor temperature conditions, building thermal characteristics, and user comfort preferences. This parameter-based decision-making optimizes the balance between energy savings and comfort maintenance for each specific situation
3Reliability
If setback control is delayed until non-occupancy is maintained for predetermined time, then false triggers are reduced, but additional energy is consumed during the delay period
Solution Approach 1:
The system performs preliminary occupancy assessment using multiple data sources including motion sensors, door locks, and historical patterns before initiating the setback timer. This multi-stage verification reduces false non-occupancy detections, ensuring setback control is only triggered when genuine absence is confirmed, thereby avoiding unnecessary energy consumption during delay periods
4Extent of automation
If motion sensor or door lock is used for occupancy detection, then occupancy status can be determined, but detection accuracy deteriorates in various situations such as blind spots or when multiple users are present
Solution Approach 1:
The patent merges multiple occupancy detection methods including motion sensors, door lock status, mobile device proximity detection, and historical occupancy patterns into a unified assessment system. By combining these diverse data sources, the system overcomes the limitations of individual sensors such as blind spots or false detections when multiple users are present, achieving more reliable and accurate occupancy determination
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. A method for controlling temperature in a temperature controlling system is provided. The method includes determining occupancy or non-occupancy of a user in a space subject to setback control; when the user's non-occupancy is determined, determining whether to start the setback control based on probability distribution of a non-occupancy period that are predetermined; when it is determined to start the setback control, determining the user's target temperature based on previously collected data; calculating a setback temperature based on the target temperature; and performing the setback control according to the calculated setback temperature.