VRF Zone Grouping Using Historical Data to Reduce Power Consumption
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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 irregular power consumption and increased utility costs due to sporadic occupation of building zones, which complicates the operation of VRF equipment and affects comfort and sustainability.
Innovation Solution
A controller for building control systems that identifies zones, analyzes data, generates zone groupings, and uses clustering algorithms to determine optimal zone groupings based on historical temperature setpoints and energy consumption, allowing for efficient generation of control signals to operate VRF equipment effectively.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Use of energy by moving object
If traditional zone grouping methods are used in VRF systems, then the system can operate with simple control logic, but the power consumption increases and utility costs rise due to inefficient zone grouping
Solution Approach 1:
The system performs preliminary analysis of historical zone data (temperature setpoints, energy consumption, occupation patterns) before operation to pre-determine optimal zone groupings. This advance preparation allows the control system to implement efficient groupings without requiring complex real-time decision-making during operation, thus reducing power consumption while maintaining manageable control complexity.
Solution Approach 2:
The control system automatically analyzes historical data and generates optimal zone groupings without requiring manual intervention or complex external optimization tools. The system serves itself by using its own operational data to improve its control strategy, reducing power consumption through self-optimized zone groupings while keeping the control system relatively simple.
2Adaptability or versatility
If manual zone grouping optimization is performed, then control logic can remain simple, but the system cannot adapt to changing occupation patterns and temperature preferences, leading to increased energy consumption
Solution Approach 1:
The system continuously collects historical data on zone occupation patterns, temperature setpoints, and energy consumption, then uses this feedback to automatically adjust and optimize zone groupings. This closed-loop approach enables the system to adapt to changing patterns while the underlying control logic remains relatively simple, as the adaptation occurs through automated data-driven grouping rather than complex control algorithms.
Solution Approach 2:
The system performs preliminary analysis of historical data to pre-determine optimal zone groupings before they are needed for control. This advance preparation allows the system to adapt to changing patterns over time while maintaining simple operational control logic, as the complex analysis work is done in advance rather than in real-time during system operation.
3Loss of energy
If multiple zone grouping iterations are performed to optimize groupings, then energy efficiency can be improved, but the time and computational resources required increase significantly
Solution Approach 1:
The system performs comprehensive zone grouping optimization in advance using historical data, so that when the system operates, the optimal groupings are already determined. This eliminates the need for multiple iterative optimizations during operation, reducing both the time required and computational resources needed, while still achieving high energy efficiency through pre-optimized groupings.
Solution Approach 2:
The system uses its own historical operational data to automatically perform the optimization work needed to improve energy efficiency. By leveraging existing data and automated analysis, the system achieves energy optimization without requiring external intervention or excessive computational resources, balancing energy efficiency improvements with acceptable time and resource investment.
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.