Air conditioner, air conditioner control method, and air conditioner control system
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing air conditioner control systems fail to prevent overcooling by adjusting the set temperature before discomfort occurs, leading to increased uncomfortable time and energy wastage.
Innovation Solution
An air conditioner system utilizing AI models to predict and adjust the set temperature and fan speed by communicating with a server device to prevent overcooling, incorporating AI processors and sensors to gather and analyze environmental data.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Object-affected harmful factors
If the air conditioner maintains a low set temperature to ensure user comfort, then user comfort is improved, but energy consumption increases and overcooling occurs
Solution Approach 1:
The system performs preliminary action by predicting future indoor temperature trends using AI models before overcooling actually occurs. The server device receives current state information, generates predicted temperature graphs, and proactively sends set temperature increase requests to prevent overcooling discomfort before it happens, thereby reducing energy consumption while maintaining comfort.
Solution Approach 2:
The system implements feedback by continuously monitoring current state information (operation time, set temperature, indoor temperature) and using AI models to generate predicted temperature graphs. The server compares these predictions with comfortable temperature graphs and adjusts the set temperature based on the predicted overcooling risk, creating a closed-loop control system that dynamically optimizes energy consumption.
2Object-affected harmful factors
If the air conditioner continuously monitors and adjusts temperature to prevent overcooling, then user comfort is improved, but system complexity increases
Solution Approach 1:
The system uses an intermediary approach by introducing a server device that handles the complex AI model computations and decision-making logic. The air conditioner itself remains relatively simple, transmitting current state information to the server and executing set temperature adjustments based on server instructions. This distributes complexity from the air conditioner to the server, making the overall system manageable.
Solution Approach 2:
The system replaces traditional rule-based mechanical control with AI-based predictive modeling. Instead of using simple if-then rules for temperature control, the system employs machine learning models (LSTM, GRU, or transformer-based models) to predict future temperature trends and generate intelligent control decisions, substituting complex mechanical control logic with sophisticated but more efficient algorithms.
Data Source
AI summary
An air conditioner comprises a memory and at least one processor individually or collectively executes the instructions to cause the air conditioner to transmit, to a server device, current state information related to the air conditioner, receive a set temperature increase request corresponding to the transmitted current state information from the server device, and adjust a set temperature of the air conditioner based on the received set temperature increase request. The set temperature increase request is received when an overcooling period, in which a first predicted temperature graph obtained from a second Artificial Intelligence (AI) model based on the transmitted current state information is reduced below a comfortable temperature graph. The comfortable temperature graph comprises an unstable period in which a comfortable temperature changes based on an operation time of the air conditioner, and a stable period in which the comfortable temperature is constantly maintained.