VRF Zone Grouping Using Clustering Algorithms for Energy Efficiency
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Solution Overview
Problem
Variable refrigerant flow (VRF) systems face challenges in optimizing zone grouping for efficient heating and cooling, leading to increased power consumption and utility costs due to unpredictable occupancy patterns across various building zones.
Innovation Solution
A controller for building control systems that identifies zones, analyzes data to generate zone groupings, and uses clustering algorithms to determine optimal zone groupings based on historical temperature setpoints and energy consumption, allowing for targeted heating or cooling operations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Use of energy by moving object
If traditional zone grouping methods are used in VRF systems, then system simplicity is maintained, but energy efficiency deteriorates due to inability to adapt to unpredictable occupancy patterns
Solution Approach 1:
The patent implements dynamic zone grouping that automatically adapts to changing occupancy patterns and thermal characteristics. The system continuously monitors zone data and reconfigures groupings in real-time, transforming static zone assignments into dynamic, responsive groupings that optimize energy efficiency as conditions change.
Solution Approach 2:
The system performs self-optimization by automatically analyzing zone data, identifying thermal similarities, and generating optimized zone groupings without requiring manual intervention. The controller autonomously adjusts zone groupings based on observed patterns, enabling the system to self-improve energy efficiency over time.
2Use of energy by moving object
If manual zone grouping is used, then system complexity is reduced, but energy efficiency deteriorates due to inability to capture thermal similarities between zones
Solution Approach 1:
The patent replaces manual zone grouping mechanisms with automated data analysis and clustering algorithms. Instead of relying on manual assessment of thermal characteristics, the system uses computational methods to objectively identify and group zones with similar thermal behaviors, eliminating human subjectivity and improving accuracy.
Solution Approach 2:
The system implements feedback loops where zone performance data is continuously collected, analyzed, and used to refine zone groupings. The controller monitors energy consumption and thermal responses, then adjusts groupings based on this feedback, creating a closed-loop system that continuously optimizes energy efficiency.
3Adaptability or versatility
If fixed zone groupings are implemented, then operational simplicity is maintained, but adaptability deteriorates in response to changing occupancy patterns
Solution Approach 1:
The system transitions from static, fixed zone groupings to dynamic groupings that automatically adjust to changing conditions. The controller continuously monitors occupancy patterns and thermal responses, reconfiguring zone groupings in real-time to maintain optimal performance as building usage patterns evolve throughout the day and week.
Data Source
AI summary
A controller for a building control system includes processors and memory storing instructions that, when executed by the processors, cause the processors to perform operations including identifying zones within a building, analyzing data associated with the zones, and generating zone groupings based on the data associated with the zones. Each of the zone groupings define zone groups and specify which of the zones are grouped together to form each of the zone groups. The operations also include identifying a particular zone grouping from zone groupings based on the data associated with zones and using the particular zone grouping to generate control signals to operate equipment of the building control system to provide heating or cooling to the zones.