Information processing apparatus and air-conditioning system provided with the same
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Solution Overview
Problem
Existing air-conditioning systems with multiple indoor units face inefficiencies in power consumption due to lack of consideration for individual heat loads, leading to increased maximum power consumption.
Innovation Solution
An information processing apparatus communicates with an air-conditioning management system to determine schedules for each indoor unit's operation based on a learning model representing the input-output relationship between influencing factors and thermal loads, allowing for optimized operation and reduced power consumption.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of energy
If the air-conditioning apparatus controls multiple indoor units without considering individual heat loads, then the control system remains simple, but electric power is wasted and maximum power consumption increases
Solution Approach 1:
The patent divides the control system into segments by creating individual learning models for each indoor unit. Each learning model independently estimates the heat load characteristics of its corresponding indoor unit based on historical operation data. This segmentation allows the system to optimize control for each unit separately, reducing overall power consumption without requiring a monolithic complex control system.
Solution Approach 2:
The learning models automatically learn and adapt to the heat load characteristics of each indoor unit through self-service mechanisms. By using historical operation data to train the models, the system enables each indoor unit to effectively 'teach' the controller its specific thermal characteristics, eliminating the need for manual configuration or complex centralized analysis.
2Productivity
If the air-conditioning apparatus uses a learning model to estimate thermal load for each indoor unit, then power consumption is reduced, but the system requires more complex data processing and model management
Solution Approach 1:
The system performs preliminary actions by pre-training learning models for each indoor unit using historical operation data before actual control operations begin. This advance preparation stores the heat load characteristics in accessible formats, allowing the control system to quickly retrieve and apply this information during operation without performing complex real-time calculations, thus improving energy efficiency while managing data processing complexity.
3Loss of energy
If the air-conditioning system shifts peak loads of multiple indoor units, then maximum power consumption is reduced, but the control scheduling becomes more complex
Solution Approach 1:
The control system dynamically adjusts the operation timing of multiple indoor units based on real-time conditions and learned heat load characteristics. Rather than using fixed schedules, the system flexibly shifts peak loads by modulating compressor start times and operational parameters, allowing it to reduce maximum power consumption while adapting to changing environmental conditions and user requirements.
Data Source
AI summary
An information processing apparatus incudes a schedule determination unit configured to determine a schedule of an operating status for causing a temperature of an air-conditioned space of each of a plurality of load-side units to reach a specified set temperature at a specified set time, based on a learning model representing an input-output relationship between input data indicating an influencing factor of a thermal load of each of the load-side units and output data indicating the thermal load.