Adaptive Comfort Temperature Control for Air Conditioning
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing air conditioning systems struggle to balance indoor comfort and energy saving, as current control methods focus mainly on energy saving and lack effectiveness in predicting and maintaining indoor thermal comfort.
Innovation Solution
The implementation of an adaptive comfort theory-based algorithm in air conditioning devices and systems, which determines a comfort temperature by calculating an exponentially-weighted running mean temperature and applying regression analysis-derived constants, allowing for dynamic control of indoor temperatures based on outdoor conditions and operation modes.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of energy
If air conditioning systems use predetermined indoor temperature control methods, then energy saving is improved, but indoor thermal comfort prediction and maintenance capability deteriorates
Solution Approach 1:
The patent changes the control parameter from fixed predetermined temperatures to dynamically calculated comfort temperatures based on outdoor temperature and adaptive comfort model parameters (A and B). This allows the system to maintain energy saving while improving thermal comfort prediction accuracy by adapting to varying environmental conditions.
Solution Approach 2:
The system transitions from static predetermined temperature control to dynamic comfort temperature control that continuously adapts to outdoor conditions. The comfort temperature is dynamically calculated using the adaptive comfort model with parameters A and B, allowing real-time optimization of both energy consumption and thermal comfort.
2Measurement precision
If additional sensors (humidity sensor, CO2 sensor, occupant detecting sensor) are installed to improve thermal comfort control, then indoor comfort prediction accuracy is improved, but device complexity and installation cost increases
Solution Approach 1:
The patent extracts the essential comfort control functionality by using only outdoor temperature data and the adaptive comfort model, eliminating the need for additional sensors. This approach maintains thermal comfort control accuracy while significantly reducing device complexity and installation requirements.
Solution Approach 2:
The system achieves multi-functionality by using a single outdoor temperature sensor to control both energy saving and thermal comfort. The adaptive comfort model processes this single input to provide comprehensive comfort control, eliminating the need for multiple specialized sensors.
3Measurement precision
If PMV index is used for indoor thermal comfort control, then theoretical comfort assessment is improved, but applicability to practical environments deteriorates
Solution Approach 1:
The patent transitions from the static PMV index to the dynamic adaptive comfort model that adapts to practical environmental conditions. The model uses outdoor temperature and location-specific parameters (A and B) to dynamically calculate comfort temperatures, making it highly applicable to real-world scenarios while maintaining theoretical rigor.
Solution Approach 2:
The system changes from using fixed PMV thresholds to using dynamically calculated comfort temperatures based on outdoor conditions and location-specific adaptive comfort parameters. This allows the system to maintain theoretical accuracy while adapting to diverse practical environments.
Data Source
AI summary
A method for controlling a temperature in an air conditioning device according to an embodiment of the present invention includes: calculating an exponentially-weighted running mean temperature for outdoor temperatures measured for a predetermined period, setting a variable constant and a fixed constant according to the exponentially-weighted running mean temperature and an operation condition, setting a comfort temperature by multiplying the exponentially-weighted running mean temperature by the variable constant and adding the fixed constant, and controlling an indoor temperature by using the set comfort temperature. Here, the fixed constant and the variable constant are constants obtained through a regression analysis of a distribution relationship between an exponentially-weighted running mean temperature and a comfort temperature, and the distribution of comfort temperatures is linearly increased from the fixed constant with a gradient of the comfort temperature according to the exponentially-weighted running mean temperature.