Systems and methods for controlling variable refrigerant flow systems using artificial intelligence
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Solution Overview
Problem
Traditional building equipment monitoring systems fail to account for various operating conditions, leading to rapid degradation and increased costs due to the difficulty and cost of installing additional sensors, especially in existing HVAC equipment, and the inability to measure certain conditions that are not measurable or feasible.
Innovation Solution
A refrigerant charge controller using machine learning models to analyze usage data from HVAC equipment, estimating refrigerant levels and initiating corrective actions, such as charging more refrigerant or repairing leaks, without the need for additional sensors.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If additional sensors are installed to monitor operating conditions, then measurement capability is improved, but device complexity and installation cost increase
Solution Approach 1:
The patent creates a virtual model (copy) of the HVAC system that replicates its behavior and states. This digital twin allows monitoring of operating conditions by simulating and inferring sensor measurements from the model rather than installing physical sensors everywhere, thereby maintaining measurement capability while reducing device complexity
Solution Approach 2:
The patent introduces an intermediary computational model that acts as a mediator between available sensor data and the desired monitoring information. The model processes limited sensor inputs to infer additional operating conditions, serving as an intermediary that translates partial measurements into comprehensive system state knowledge without requiring direct physical sensors for all parameters
2Measurement precision
If sensors are embedded into existing HVAC equipment, then measurement accuracy is improved, but installation difficulty and cost increase
Solution Approach 1:
Instead of embedding physical sensors into existing HVAC equipment, the patent creates a virtual representation that replicates sensor functionality through computational modeling. The digital twin infers measurements that would otherwise require embedded sensors, eliminating the need for physical installation while maintaining measurement capability
Solution Approach 2:
The patent replaces the mechanical/physical sensor embedding approach with a computational/software-based solution. Rather than physically installing sensors into HVAC equipment, the system uses algorithms and models to calculate and infer operating conditions from available data, substituting mechanical installation with computational processing
3Device complexity
If traditional monitoring systems are used, then system simplicity is maintained, but equipment degradation accelerates due to unmonitored conditions
Solution Approach 1:
The patent performs preliminary monitoring and analysis by continuously tracking operating conditions through the digital twin model. By proactively identifying trends and potential issues before they cause degradation, the system enables preventive maintenance actions that extend equipment lifespan while maintaining relatively simple system architecture
Solution Approach 2:
The patent implements a feedback mechanism where the digital twin continuously compares predicted system behavior with actual measurements, identifying deviations that indicate potential problems. This feedback loop enables early detection of degradation trends and triggers appropriate maintenance actions, improving reliability without requiring complex additional hardware
Data Source
AI summary
A refrigerant charge controller for heating, ventilation, or air conditioning (HVAC) equipment includes a processing circuit configured to analyze usage data for the HVAC equipment using a machine learning model to estimate an amount of refrigerant used by the HVAC equipment, identify a refrigerant deficiency based on the amount of refrigerant, and initiate a corrective action in response to identifying the refrigerant deficiency.