Air-conditioner based on parameter learning using artificial intelligence, cloud server, and method of operating and controlling thereof
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Solution Overview
Problem
Air conditioners lack dynamic adaptation to changing environmental conditions, such as external temperature and humidity, which limits their operational efficiency in switching between rapid and comfortable modes.
Innovation Solution
A learning-based system that generates parameters during operation and adjusts cooling capacity, using a parameter generator and learning unit to determine optimal operation modes, with a cloud server transmitting operation mode information to air conditioners for efficient energy use.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Speed
If the air conditioner operates in rapid mode with maximum cooling capacity, then the cooling speed is improved, but the electrical power consumption increases
Solution Approach 1:
The air conditioner dynamically switches between rapid mode and comfortable mode based on real-time temperature monitoring. The system starts in rapid mode to quickly cool the space, then transitions to comfortable mode when the target temperature is approached, optimizing the balance between cooling speed and energy consumption throughout the operation cycle
Solution Approach 2:
The control unit implements periodic monitoring of indoor temperature and periodically switches between operation modes. The system alternates between rapid cooling periods and comfortable maintenance periods, creating a rhythmic operation pattern that reduces overall power consumption while maintaining effective cooling
2Use of energy by moving object
If the air conditioner operates in comfortable mode with adjusted cooling load, then the electrical power consumption is reduced, but the cooling speed decreases
Solution Approach 1:
The air conditioner performs preliminary rapid cooling to quickly reduce the temperature gap between indoor and outdoor environments. By pre-cooling the space to near target temperature before switching to comfortable mode, the system minimizes the time needed in high-power rapid mode, thereby reducing overall energy consumption
3Device complexity
If the air conditioner uses fixed operation modes, then the control system is simple, but the adaptability to changing environmental conditions deteriorates
Solution Approach 1:
The control unit continuously monitors indoor temperature and uses this feedback to dynamically adjust operation modes. The system compares current temperature with target temperature and automatically switches between rapid and comfortable modes, providing real-time adaptation to environmental conditions without requiring complex user intervention
Solution Approach 2:
The air conditioner autonomously determines when to switch between operation modes based on temperature conditions. The control unit self-adjusts the cooling strategy without external input, automatically transitioning from rapid to comfortable mode when appropriate, thereby achieving environmental adaptability through self-service control
Data Source
AI summary
An air conditioner includes: a blower configured to discharge air, the blower being connected to an outdoor unit, a parameter generator configured to generate at least one parameter during a time period for which the air conditioner is operated with a first cooling capacity based on a set temperature, a learning unit configured to receive the generated at least one parameter as a learning factor and generate operation mode information, an operation mode controller configured to control at least one of the blower or the outdoor unit based on the generated operation mode information, and a central controller configured to control the parameter generator, the learning unit, and the operation mode controller. The air conditioner is operated with a second cooling capacity after the air conditioner is operated with the first cooling capacity for the time period, the second cooling capacity being different from the first cooling capacity.