Air conditioning system, server system, network, method for controlling an air conditioning system and method for controlling a network
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Solution Overview
Problem
Current air conditioning systems rely heavily on manual commissioning by expert installers, leading to inefficient operation and thermal discomfort due to variations in system dynamics over time, such as changes in emitter size, space use, or occupant activity, and often result in suboptimal performance compared to theoretical expectations.
Innovation Solution
An air conditioning system with a controller connected to a network for real-time data exchange, allowing for automated self-tuning of system control settings using aggregated network data and predictive models to optimize operational parameters like flow temperature and compressor frequency, adapting to changes in building dynamics without manual intervention.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If manual commissioning by expert installers is used, then initial system configuration can be completed, but system performance deteriorates over time due to changes in system dynamics and cannot adapt to changes automatically
Solution Approach 1:
The system performs self-tuning by automatically monitoring its own operational parameters and dynamically adjusting control settings without requiring manual intervention from experts. The controller continuously optimizes system performance based on real-time data from sensors and actuators, enabling the system to service and adapt itself autonomously
Solution Approach 2:
The system implements continuous feedback loops where sensors monitor operational parameters (temperatures, pressures, flows) and feed this data back to the controller. The controller processes this feedback and dynamically adjusts actuator positions and operational settings to maintain optimal performance as system dynamics change over time
2Productivity
If manual commissioning is performed, then system parameters can be configured initially, but operation efficiency decreases due to inability to detect and correct suboptimal performance over time
Solution Approach 1:
The system maintains continuous optimization of operational parameters through ongoing monitoring and adjustment. Rather than relying on periodic manual interventions, the controller continuously adapts control settings to maintain peak efficiency, ensuring uninterrupted optimal performance as system conditions evolve
Solution Approach 2:
The patent replaces the mechanical process of manual commissioning and adjustment with an automated electronic control system. Sensors, communication interfaces, and algorithms substitute for human experts, enabling automatic detection and correction of performance deviations without physical intervention
3Reliability
If manual commissioning checklists are used, then system configuration can be documented, but thermal discomfort occurs due to variations in installer expertise and inability to adapt to changing conditions
Solution Approach 1:
The system transitions from static initial configuration to dynamic continuous optimization. Control parameters are no longer fixed but continuously adjusted based on real-time monitoring of system performance and environmental conditions, enabling the system to adapt dynamically to changing occupancy patterns, weather conditions, and system degradation
4Loss of energy
If expert commissioning is performed, then initial performance can be optimized, but system sizing and balancing issues remain undetected leading to inefficient operation
Solution Approach 1:
The system implements comprehensive feedback monitoring of operational parameters including temperatures, pressures, and flows throughout the system. This continuous data collection enables detection of inefficiencies such as improper system sizing, balancing issues, and component malfunctions by comparing actual performance against expected performance thresholds
Solution Approach 2:
The patent introduces an intermediary communication interface and data processing layer between sensors and the controller. This intermediary layer aggregates data from multiple sources, processes it through algorithms, and presents actionable insights for detecting and diagnosing system inefficiencies that would be difficult to identify through manual observation alone
Data Source
Figure 1
AI summary
The present invention refers to an air conditioning system, a server system, a network as well as a method for controlling an air conditioning system and a method for controlling a network.