HVAC Model Identification Under Comfort-Neutral Testing
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
The setup of multi-input-multi-output (MIMO) control systems for HVAC systems is complex and requires skilled control engineers, leading to high costs and potential disruptions in building comfort during testing, as it involves setting relations between manipulated and controlled variables and a cost objective function.
Innovation Solution
A method that automatically identifies a steady-state HVAC system model and cost objective model by pairing manipulated and controlled variables, perturbing a variable to maintain comfort conditions, and deriving a model without expert intervention, using proportional integral control and look-up tables to ensure comfort is maintained during system exploration.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional step-testing method is used for MIMO controller setup, then control model identification can be achieved, but building comfort is disrupted and high expert costs are incurred
Solution Approach 1:
The system preemptively counteracts potential comfort disruptions by implementing comfort constraints before testing begins. The comfort-safe step testing methodology pre-defines acceptable ranges for controlled variables, ensuring that any perturbations applied during identification maintain inhabitant comfort throughout the process.
Solution Approach 2:
The system continuously monitors controlled variables during the identification process and uses feedback mechanisms to adjust perturbations. By measuring actual system responses and comparing them against comfort thresholds, the controller adapts its testing strategy in real-time to prevent comfort violations while still gathering necessary identification data.
2Measurement precision
If traditional step-testing method is used for MIMO controller setup, then control model identification can be achieved, but expert control engineers are required leading to high costs
Solution Approach 1:
The system performs self-identification by automatically selecting perturbations, executing tests, collecting data, and generating the control model without human intervention. The automated methodology includes built-in logic for variable pairing, perturbation selection, and model derivation, enabling the HVAC system to configure its own MIMO controller through comfort-safe step testing.
Solution Approach 2:
The system prepares all necessary identification parameters, variable pairings, and comfort constraints in advance before actual testing begins. This preliminary configuration includes defining the cost objective function, setting measurement thresholds, and pre-planning the perturbation sequence, which eliminates the need for expert engineers during the actual identification process.
3Use of energy by moving object
If MIMO control is implemented, then energy optimal control can be achieved, but controller setup becomes significantly more difficult
Solution Approach 1:
The system replaces manual expert configuration with an automated identification process. Instead of requiring control engineers to manually pair variables and tune parameters, the system uses automated step testing with computational algorithms to derive the control model and configure the MIMO controller, substituting mechanical expert knowledge with automated computational methods.
Solution Approach 2:
The system systematically varies operating parameters during the identification process to map system behavior across different conditions. By changing manipulated variables within comfort constraints and measuring resulting controlled variable responses, the system builds a comprehensive control model that captures energy optimization opportunities without requiring expert parameter tuning.
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
Enables a plug-and-play, comfort-safe setup of multivariable controllers, reducing the need for expert control engineers and minimizing disruptions, allowing for cost-optimal control of HVAC systems without compromising inhabitant comfort.
Implementation Method 1
controlling the HVAC system to maintain controlled variables in a comfort range
Data Source
AI summary
A method includes pairing manipulated variables and controlled variables in an HVAC system, perturbing a variable, controlling the HVAC system to maintain controlled variables in a comfort range, determining a state of the system, and deriving a model from the state of the system.