Air-conditioning system, machine learning device, and machine learning method
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Solution Overview
Problem
Existing air-conditioning systems face high workload in optimizing device operating capacity post-installation, requiring extensive data measurement to construct accurate models for energy efficiency and comfort.
Innovation Solution
A machine learning apparatus and method that acquires state variables from both outside and inside air conditioning devices, performs reinforcement learning to associate these variables with energy consumption, and calculates rewards to optimize the operating capacity of outside and air conditioning devices, reducing the need for extensive post-installation data measurement and workload.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Use of energy by moving object
If a model is constructed to determine operating capacity of air conditioning devices, then energy efficiency and comfort are improved, but post-installation workload increases due to extensive data measurement requirements
Solution Approach 1:
The patent applies preliminary action by pre-collecting and storing operation data during the manufacturing or installation phase before actual use. The data collection device gathers temperature, humidity, and device operation data in advance, creating a ready-to-use database that eliminates the need for extensive post-installation measurements. This allows the model to be constructed and deployed immediately, improving energy efficiency without burdening users with data collection work.
2Measurement precision
If device characteristics are accurately reflected in the model, then optimization precision is improved, but data measurement and model construction complexity increases
Solution Approach 1:
The patent applies self-service by enabling the system to automatically collect, store, and process its own operation data without external intervention. The data collection device autonomously gathers temperature, humidity, and device operation data, and the model construction unit automatically processes this data to create accurate device-specific models. This automation maintains high model accuracy while eliminating the complexity of manual data collection and model building.
3Loss of energy
If reinforcement learning is used to optimize operating capacity, then energy consumption is reduced, but system complexity and computational requirements increase
Solution Approach 1:
The patent applies preliminary action by pre-collecting comprehensive operation data and constructing training datasets before deploying reinforcement learning. The system gathers historical data on temperature, humidity, and device operations, then uses this pre-processed data to train the reinforcement learning model offline. This approach reduces energy consumption during actual operation while managing system complexity by performing computationally intensive tasks in advance rather than in real-time.
Data Source
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AI summary
An air-conditioning system that optimizes operation capacity of an outside air conditioning unit and operation capacity of an air conditioning unit is provided. An air-conditioning system includes an outside air conditioning device, an air conditioning device, and a machine learning apparatus, and includes a state variable acquiring unit configured to acquire state variables including a condition of outside air, a condition of inside air, an operation condition of the outside air conditioning device, an operation condition of the air conditioning device, and a temperature or humidity set for a target space, a learning unit configured to perform learning by associating the state variables with at least either the operating capacity of the outside air conditioning device or the operating capacity of the air conditioning device, and a reward calculating unit configured to calculate a reward that correlates with a total of energy consumption of the outside air conditioning device and energy consumption of the air conditioning device. The learning unit performs the learning by using the reward.