Thermal Comfort Control Using Regression-Based Preheating Timing
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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 determine the calculated time 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 for improved accuracy, allowing for reduced sensor usage and efficient operation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
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 to ensure comfort, but energy is wasted due to excessive heating
Solution Approach 1:
The system performs preliminary heating before occupancy based on predicted arrival time, but controls the heating to stop exactly when the minimum thermal comfort temperature is reached, avoiding excessive heating. This is achieved through continuous temperature monitoring and automated HVAC control that shuts off heating at the precise moment the target temperature is achieved.
Solution Approach 2:
The system continuously monitors the zone temperature and uses this feedback to control the HVAC operation. When the temperature reaches the minimum thermal comfort zone threshold, the system receives feedback and automatically adjusts or shuts off heating, preventing energy waste while ensuring comfort is achieved.
2Loss of energy
If the HVAC system does not heat the zone to reach the minimum temperature of the thermal comfort zone by the time the zone becomes occupied, then energy is saved, but occupant discomfort occurs
Solution Approach 1:
The system initiates heating in advance of occupancy based on predicted arrival time, ensuring the zone reaches the minimum thermal comfort temperature before occupants arrive. This preliminary action is timed precisely to start heating early enough to achieve comfort without excessive energy consumption.
Solution Approach 2:
The system dynamically adjusts the heating operation based on real-time temperature conditions and predicted occupancy timing. The HVAC control is not static but adapts to the actual thermal response of the zone, modifying heating intensity and duration to precisely achieve the target temperature at the right moment.
3Use of energy by stationary object
If the HVAC system operates in passive mode during unoccupied periods allowing temperature to drop, then energy is saved, but the zone may not reach thermal comfort temperature when occupied
Solution Approach 1:
The system transitions from passive mode to active heating mode in advance of predicted occupancy, initiating preliminary heating action. This allows the system to benefit from energy savings during unoccupied periods while ensuring comfortable temperature is achieved before occupants arrive, eliminating the trade-off between energy saving and temperature control reliability.
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
This approach ensures the thermal comfort system operates at the correct time to reach the target temperature, minimizing energy waste and occupant discomfort by providing a precise calculation of the required operation time, thus optimizing energy usage.
Implementation Method 1
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
Data Source
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.