AI-Driven Air Conditioner User Identification for Personalized Cooling
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Solution Overview
Problem
Conventional air conditioners do not provide personalized cooling based on the preferences of multiple users within a cooling space, leading to suboptimal cooling performance.
Innovation Solution
An air conditioner that identifies users within the space using artificial intelligence and adjusts settings based on reinforcement learning models, prioritizing members and updating models with feedback to optimize cooling preferences.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If conventional air conditioners provide cooling regardless of who is present, then the system is simple and easy to operate, but the cooling performance is not optimized for individual users
Solution Approach 1:
The air conditioner incorporates a feedback mechanism where user responses (likes/dislikes) to recommended cooling settings are continuously collected and used to update the reinforcement learning model. This allows the system to learn and adapt to individual user preferences over time, improving cooling performance optimization while managing complexity through iterative learning rather than complex pre-programming
Solution Approach 2:
The reinforcement learning model enables the system to autonomously identify users, recommend optimal cooling settings, and improve its performance automatically without requiring manual configuration or complex user input. The system serves itself by continuously learning from user feedback and adjusting its behavior, reducing the need for complex manual control interfaces
2Adaptability or versatility
If the air conditioner uses reinforcement learning to identify and prioritize multiple users, then personalized cooling is achieved, but the device complexity increases
Solution Approach 1:
The system segments the cooling control into individual user profiles, with each user having their own preference data stored in the reinforcement learning model. By dividing the overall cooling control into separate learnable segments for each user, the system achieves personalized cooling capability while managing complexity through modular organization of user-specific parameters and preferences
Solution Approach 2:
The reinforcement learning model dynamically adjusts cooling parameters (temperature, airflow, timing) based on learned user preferences and current conditions. By changing parameters adaptively rather than using fixed complex control logic, the system achieves high adaptability for personalized cooling while keeping the underlying control mechanism relatively simple through data-driven parameter adjustment
3Reliability
If the air conditioner continuously updates learning models with user feedback, then cooling performance is continuously enhanced, but the use of energy increases
Solution Approach 1:
The reinforcement learning model updates are performed periodically based on accumulated user feedback rather than continuously in real-time. The system collects user responses and performs model updates at appropriate intervals, balancing the need for continuous performance enhancement with energy conservation by avoiding constant computational updates during normal operation
Data Source
AI summary
Discussed is an air conditioner disposed in an indoor space. The air conditioner includes a sensor, a memory configured to store a plurality of learning results respectively corresponding to a plurality of members, and a processor configured to identify at least one member that is present in the indoor space among the plurality of members by using data acquired by the sensor, control operation of at least one of a compressor, a fan motor, or a vane motor based on the learning result corresponding to an identified member so as to adjust a set value including at least one of a set temperature, an air volume, or a wind direction, and update the learning results by using feedback on the adjusted set value.