Machine learning apparatus for determining operation condition of precooling operation or preheating operation of air conditioner
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Solution Overview
Problem
Existing air conditioner control systems do not utilize machine learning to optimize precooling and preheating operations, which can lead to suboptimal user comfort and energy efficiency.
Innovation Solution
A machine learning apparatus that acquires and learns from state variables such as room temperature, set temperature, and outside air temperature data to determine the operation conditions of precooling and preheating operations, including the use of a reward system to optimize energy usage and comfort.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional control methods are used for precooling and preheating operations, then the system structure remains simple, but user comfort and energy efficiency are suboptimal
Solution Approach 1:
The control system performs self-learning through machine learning algorithms, automatically optimizing precooling and preheating operations without requiring external intervention or complex manual configuration. The system learns from historical data and autonomously determines optimal control parameters, achieving high user comfort while maintaining relatively simple system architecture.
Solution Approach 2:
The patent replaces traditional rule-based mechanical control systems with intelligent machine learning algorithms. Instead of using fixed control rules and manual parameter settings, the system uses data-driven models to automatically determine optimal operation conditions, thereby improving comfort and energy efficiency without proportionally increasing physical system complexity.
2Loss of energy
If machine learning is introduced to optimize precooling and preheating operations, then energy efficiency and user comfort improve, but the device complexity increases
Solution Approach 1:
The machine learning system performs self-learning and automatic optimization of energy consumption parameters. The control device autonomously analyzes historical operation data, identifies energy-saving patterns, and adjusts precooling and preheating parameters without external intervention, achieving reduced energy loss while keeping the added complexity manageable through automated operation.
Solution Approach 2:
The system optimizes multiple operational parameters including precooling/preheating duration, temperature setpoints, and compressor operation timing. By dynamically adjusting these parameters based on learned patterns from historical data, the system achieves significant energy savings while the complexity is confined to software algorithms rather than hardware modifications.
3Productivity
If machine learning is used to determine operation conditions, then productivity and energy efficiency improve, but the difficulty of detecting and measuring optimal parameters increases
Solution Approach 1:
The machine learning system continuously monitors actual operation results and energy consumption, using this feedback to iteratively improve its control strategies. The system learns from historical data including outdoor temperature, humidity, and user presence information, automatically adjusting parameters to optimize productivity and energy efficiency without requiring manual measurement or detection of optimal conditions.
Solution Approach 2:
The patent replaces manual parameter detection and optimization processes with automated machine learning algorithms. Instead of requiring experts to manually determine optimal operating parameters through complex measurements and analysis, the system automatically processes sensor data and learns optimal control strategies, significantly reducing the difficulty of parameter optimization while improving system efficiency.
Data Source
AI summary
A machine learning apparatus determines an operation condition of a precooling operation or preheating operation of an air conditioner. The machine learning apparatus includes an acquisition unit and a learning unit. The acquisition unit acquires, as state variables, room temperature data at a time of the precooling operation or preheating operation, set temperature data, and outside air temperature data. The learning unit learns the operation condition of the precooling operation or preheating operation based on the state variables, a room temperature after start of the precooling operation or preheating operation, and a set temperature.