Central plant control system with computation reduction based on sensitivity analysis
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Solution Overview
Problem
Conventional methods for predicting thermodynamic states and optimizing operating parameters of HVAC systems are computationally inefficient, particularly when dealing with complex arrangements and multiple sets of operating parameters, leading to excessive resource consumption and exhaustive computations.
Innovation Solution
A controller and method that generate gradient data to identify a reduced group of control variables, excluding those insensitive to performance, allowing for efficient operation by predicting states using a non-linear optimizer and determining optimal values for the HVAC system, thereby reducing computational resources and improving performance.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional non-linear solver is used to predict thermodynamic states for multiple candidate sets of operating parameters, then accurate prediction is achieved, but computational resources and time are excessively consumed
Solution Approach 1:
The patent segments the control variables into two distinct groups: sensitive control variables that significantly affect operating performance and insensitive control variables that have minimal impact. This segmentation allows the system to focus computational efforts only on the sensitive variables, thereby reducing the overall computational burden while maintaining prediction accuracy for the most critical parameters.
Solution Approach 2:
The patent extracts and identifies the sensitive control variables from the complete set of control variables through sensitivity analysis. By taking out only the essential sensitive variables that truly influence system performance, the system eliminates redundant computations associated with insensitive variables, thus significantly reducing computation time and resource consumption.
2Reliability
If full thermodynamic states are computed for all control variables, then comprehensive system analysis is achieved, but computational complexity and resource usage increase
Solution Approach 1:
The patent divides the control variables into sensitive and insensitive groups, computing thermodynamic states only for the sensitive variables. This segmentation maintains reliable system analysis by focusing on the variables that actually drive system behavior, while avoiding the computational complexity of analyzing all variables in detail.
Solution Approach 2:
The patent applies local quality by providing detailed thermodynamic state computation only where it matters most - for the sensitive control variables that significantly impact operating performance. For insensitive variables, the system uses simplified approaches, thereby reducing overall computational complexity while maintaining analysis reliability in the critical areas.
3Productivity
If multiple sets of operating parameters are evaluated to determine optimal operation, then operating performance is optimized, but computational resources are exhausted
Solution Approach 1:
The patent extracts and evaluates only the sensitive control variables when comparing multiple sets of operating parameters. By taking out the essential variables that determine operating performance and ignoring the insensitive ones, the system achieves effective optimization while dramatically reducing the computational energy required to evaluate multiple candidate solutions.
Solution Approach 2:
The patent applies partial action by computing thermodynamic states for only the necessary sensitive control variables rather than all possible variables. This partial computation approach provides sufficient information for optimizing operating efficiency without the excessive computational energy expenditure of analyzing every possible parameter combination.
Data Source
AI summary
Disclosed herein are related to a system, a method, and a non-transitory computer readable storing instructions for operating an energy plant comprised of heating, ventilation and air conditioning (HVAC) devices. In one aspect, the system generates gradient data indicating a gradient of operating performance of the energy plant with respect to values of a plurality of control variables of HVAC devices. The system determines, from the plurality of control variables, a reduced group of control variables of the HVAC devices based on the gradient data. The system determines a set of values of the reduced group of control variables. The system operates the energy plant according to the determined set of values of the reduced group of control variables.