Information processing device and air conditioning system
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Solution Overview
Problem
Large-scale air conditioning systems face inefficiencies due to varying outdoor environments such as weather, wind direction, and temperature, leading to potential power loss if outdoor unit operations are not optimized for these conditions.
Innovation Solution
An information processing device that acquires weather forecast and compressor data to generate models for inferring refrigerant pressures and frequencies, allowing for efficient operation of multiple outdoor units by adjusting compressor frequencies and capacities based on environmental conditions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of energy
If outdoor unit operation is controlled without considering outdoor environments, then operation simplicity is maintained, but power loss occurs due to poor operation efficiency
Solution Approach 1:
The system performs preliminary acquisition of weather forecast information, compressor frequency data, and refrigerant pressure data before optimization control is needed. By pre-collecting this environmental and operational data, the system can predict optimal operating conditions in advance, reducing power loss without requiring complex real-time decision-making infrastructure.
Solution Approach 2:
The system establishes a feedback loop by continuously monitoring outdoor environmental conditions (weather, temperature, wind) and compressor operational parameters (frequency, refrigerant pressure). This feedback mechanism enables the controller to adjust compressor operations dynamically based on actual environmental conditions, optimizing energy efficiency while maintaining manageable system complexity through automated closed-loop control.
2Use of energy by moving object
If compressor frequency is adjusted to optimize operation efficiency, then energy efficiency improves, but control complexity increases
Solution Approach 1:
The system optimizes compressor energy efficiency by dynamically adjusting operational parameters including compressor frequency, refrigerant pressure levels, and capacity settings. By changing these parameters based on acquired environmental and operational data, the system achieves better energy efficiency. The parameter-based approach allows for systematic optimization without requiring fundamental redesign of the control architecture.
3Power
If multiple outdoor units are operated simultaneously, then air conditioning capacity increases, but energy consumption increases
Solution Approach 1:
The system applies partial action by operating only the necessary number of outdoor units based on actual cooling or heating demands and environmental conditions. Rather than running all outdoor units simultaneously, the controller selectively activates individual units or adjusts their capacity levels, achieving required air conditioning capacity while minimizing energy consumption through optimized unit selection and partial-load operation.
Data Source
AI summary
A first learning device includes a data acquisition unit and a model generation unit. The data acquisition unit includes the data acquisition unit to acquire, as first learning data, information on a weather forecast for an area where a plurality of outdoor units are installed, information on time, information on a frequency of each of a plurality of compressors included in each of the plurality of outdoor units, information on a low pressure of each of the compressors, and information on a high pressure of each of the compressors; and the model generation unit to generate, using the first learning data, a first learned model for inferring the low pressure and the high pressure from the information on the weather forecast and the information on the frequency.