Thermodynamic Zone Modeling for HVAC Load Estimation
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Solution Overview
Problem
Conventional temperature control systems in interior spaces, such as HVAC and refrigeration systems, are limited by the type of feedback data they provide, which restricts system diagnostics and accuracy, leading to inefficient operation and potential adverse effects on food quality in refrigeration systems.
Innovation Solution
A method using a reduced order thermodynamic model and an Extended Kalman Filter to estimate heating/cooling loads and temperatures in zones, allowing for the identification of unknown parameters and states, thereby enhancing monitoring and control precision.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional sensor-based monitoring is used, then system simplicity is maintained, but measurement precision and diagnostic accuracy are limited
Solution Approach 1:
The patent introduces a reduced-order thermodynamic model as an intermediary between available sensor measurements and the desired temperature estimates. This model acts as a mediator that transforms limited measurement data into comprehensive thermal state information, achieving high measurement precision without requiring additional complex sensors or measurement devices.
Solution Approach 2:
The patent replaces direct physical measurement mechanisms (additional temperature sensors throughout the space) with a computational approach using thermodynamic models and data fusion algorithms. This substitution eliminates the need for complex mechanical sensing infrastructure while achieving superior temperature estimation accuracy through mathematical modeling and information processing.
2Loss of time
If manual diagnostic processes are used, then system complexity is minimized, but loss of time in diagnosing issues increases
Solution Approach 1:
The patent implements preliminary action by continuously running the thermodynamic model and data fusion algorithm in real-time, maintaining up-to-date estimates of thermal states and diagnostic information before actual diagnostic needs arise. This allows immediate identification of temperature deviations and their causes without waiting for manual investigation, significantly reducing diagnostic time while automating the monitoring process.
3Loss of information
If limited sensor data is used, then device complexity is reduced, but loss of information for system diagnostics increases
Solution Approach 1:
The patent implements a feedback mechanism where the thermodynamic model continuously processes available sensor measurements and generates comprehensive thermal state estimates, which are then fed back into the system for monitoring and control decisions. This feedback loop transforms limited sensor data into rich diagnostic information, enabling thorough system monitoring without requiring a proportional increase in sensor infrastructure or system complexity.
Data Source
AI summary
A method for estimating a heating/cooling load of a zone within a building may include determining a measured parameter from the zone, generating a reduced order thermodynamic model of the zone, generating an Extended Kalman Filter based on the thermodynamic model of the zone, and processing the measured parameter using the Extended Kalman Filter to estimate at least one unknown state of the zone, such as an estimated load. A similar method may be used to estimate a temperature in a cold room of a refrigeration system.