Operation of a thermal comfort system
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Solution Overview
Problem
HVAC systems face challenges in efficiently reaching and maintaining a thermal comfort zone upon occupancy, often resulting in energy wastage and occupant discomfort due to incorrect timing of heating or cooling operations.
Innovation Solution
A regression model is used to calculate the time required for a thermal comfort system to reach a target temperature, considering the target temperature, actual zone temperature, and ambient air temperature, which can be updated with each run cycle to improve accuracy, allowing for reduced energy usage and enhanced comfort.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Temperature
If the HVAC system heats the zone to a temperature above the minimum temperature of the thermal comfort zone before occupancy, then the zone temperature is increased, but energy is wasted
Solution Approach 1:
The system performs preliminary heating or cooling before occupancy based on predicted arrival time, but stops exactly when the thermal comfort zone is reached rather than continuing to heat, thereby avoiding energy waste from excessive temperature elevation
Solution Approach 2:
The system continuously monitors zone temperature and uses feedback control to stop heating or cooling when the target temperature range is achieved, preventing energy waste from overheating or overcooling the space
2Use of energy by moving object
If the HVAC system does not heat the zone to the minimum temperature of the thermal comfort zone before occupancy, then energy is saved, but occupant discomfort occurs
Solution Approach 1:
The system performs preliminary heating or cooling before occupancy based on predicted arrival time and learned thermal characteristics, ensuring the zone reaches the minimum temperature of the thermal comfort zone exactly when occupants arrive, thereby guaranteeing comfort while minimizing energy consumption
Solution Approach 2:
The system uses learned thermal models of the specific zone to predict heating/cooling requirements and timing, allowing it to autonomously determine when and how much to heat or cool to achieve comfort at occupancy without manual intervention
3Device complexity
If the HVAC system uses a simple temperature control approach, then the system complexity is reduced, but the timing precision to reach thermal comfort zone is insufficient
Solution Approach 1:
The system automatically learns the thermal characteristics of each zone through repeated operation cycles, building predictive models that enable precise timing of heating/cooling operations without requiring complex manual configuration or oversimplified control logic
Data Source
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AI summary
Systems, methods, and devices for operation of a thermal comfort system are described herein. For example, one or more embodiments include receiving data, including a target temperature for a zone, an actual temperature of the zone, and an ambient temperature of air being supplied to a thermal comfort system, and determining, through a regression model, from the received data, a calculated time when the zone will reach the target temperature upon operation of the thermal comfort system.