AHU Rule Conflict Ranking for Energy and Comfort Control
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Conventional Air Handling Units (AHUs) face challenges in optimizing performance parameters due to conflicting rules that lead to increased energy consumption and discomfort levels, resulting from heuristic rule-based systems lacking expertise and potential conflicts among stakeholders.
Innovation Solution
A processor-implemented method and system that utilize a test case generation framework to identify and rank rule sets with conflicting conditions, dynamically optimizing parameters such as energy consumption and discomfort levels by selecting rule sets based on highest conflict frequency, and applying a rule flipping mechanism to balance energy and comfort.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If heuristic rule-based control systems are used in AHUs, then operational knowledge and intuitive understanding can be applied, but conflicting rules arise leading to increased energy consumption and device duty cycling
Solution Approach 1:
The system implements a feedback mechanism where the verification framework analyzes the behavior of control rules under various test cases, identifies conflicts, and provides information to operators for rule refinement. This closed-loop approach allows the system to learn from conflicts and improve rule quality over time, reducing energy consumption while maintaining operational expertise.
Solution Approach 2:
The verification framework performs preliminary analysis of control rules before deployment by generating test cases that simulate various operating conditions. This preliminary verification identifies potential conflicts and violations before they occur in the actual system, allowing operators to refine rules proactively and avoid energy-wasting conflicts during operation.
2Adaptability or versatility
If multiple stakeholders provide control rules, then diverse operational perspectives are incorporated, but rule conflicts increase causing device duty cycling and equipment stress
Solution Approach 1:
The verification framework acts as an intermediary between multiple stakeholders' control rules. It objectively analyzes rules from different sources, identifies conflicts, and provides structured feedback for resolution. This mediator approach maintains the benefits of diverse perspectives while systematically eliminating conflicts that would otherwise cause equipment stress and reliability issues.
Solution Approach 2:
The system segments the rule verification process into distinct components: test case generation, conflict identification, violation detection, and feedback provision. This segmentation allows each aspect of rule quality to be analyzed independently and systematically, making it easier to manage complex multi-stakeholder rule sets while maintaining equipment reliability.
3Adaptability or versatility
If control rules are frequently adjusted to optimize performance, then system adaptability improves, but constraint violations occur affecting occupant comfort and equipment performance
Solution Approach 1:
The verification framework provides beforehand cushioning by pre-identifying potential constraint violations in control rules before they are implemented. By simulating various operating conditions and detecting violations in advance, the system prevents comfort and equipment constraints from being violated, allowing safe optimization without harmful side effects.
Data Source
Figure 1
Figure 2
Figure 3
AI summary
Sub-systems of air handling units in infrastructures face unresolved problem of conflict in the rules that activate in a contradictory manner at the same time resulting in sub-optimal performance of the subsystems. The present disclosure provides a system and method for optimizing performance parameters of air handling units in infrastructures. Rule sets having conflicting conditions are identified after verification of rules which are specific to air handling units. Further, frequency of the rule sets having conflicting conditions is determined to generate a ranked list of the rule sets having conflicting conditions. Another ranking procedure is implemented for the rules comprised in the ranked list of the rule sets having conflicting conditions. The system dynamically optimizes one or more parameters specific to the performance criteria based on the ranking of rules.