HVAC Supervisory Control Using Zone Demand Signals
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Solution Overview
Problem
Existing HVAC supervisory controllers are difficult, costly, and time-consuming to install and maintain, especially in large multi-zone buildings, and often require retrofitting and expert installation, while also being insensitive to zone temperature variations and nonlinear relationships with set points, leading to conflicts with local controllers and occupant discomfort.
Innovation Solution
A supervisory controller that uses a data management module to receive zone demand signals and a parameter identification module to determine comfort states based on a best model structure, allowing for efficient energy distribution without requiring expert installation and working within existing infrastructure, focusing on critical zones with fluctuating demand signals.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If a supervisory controller controls all local zone controllers in a large multi-zone building, then comprehensive HVAC control is achieved, but device complexity and installation difficulty increase significantly
Solution Approach 1:
The supervisory controller segments the building into critical zones and non-critical zones, focusing control efforts only on critical zones. This segmentation reduces the dimensionality of the control problem while maintaining comprehensive control where it matters most, resolving the contradiction between comprehensive control and controller complexity.
Solution Approach 2:
The system applies different control strategies to different zones based on their criticality. Critical zones receive active supervisory control while non-critical zones rely on local controllers, creating local quality variations in control intensity that reduce overall system complexity while maintaining necessary control coverage.
2Measurement precision
If zone temperature is used to determine comfort state, then comfort monitoring is achieved, but conflicts arise with local zone controllers and installation becomes difficult
Solution Approach 1:
The system uses zone demand signals from local controllers as an intermediary indicator of comfort state, rather than directly measuring zone temperature. This intermediary approach allows the supervisory controller to infer comfort status without directly conflicting with local controllers, and enables installation using existing infrastructure without additional temperature sensors in each zone.
Solution Approach 2:
The system utilizes feedback from zone demand signals to determine comfort state. By monitoring whether zones are requesting heating or cooling, the supervisory controller can infer comfort status without direct temperature measurement, avoiding conflicts with local controllers while maintaining accurate comfort monitoring.
3Reliability
If existing supervisory controllers are installed in large multi-zone buildings, then HVAC control capability is achieved, but installation and maintenance become costly and time-consuming requiring expert personnel
Solution Approach 1:
The supervisory controller automatically identifies critical zones and configures control parameters without requiring expert installation. The system performs self-configuration by analyzing zone characteristics and demand patterns, enabling standard personnel to install and maintain the system while maintaining reliable HVAC control capability.
Solution Approach 2:
The system performs preliminary identification of critical zones and configuration of control strategies during initial operation, before full deployment. This preliminary action enables the system to adapt to the specific building characteristics automatically, eliminating the need for expert installation and manual configuration while ensuring reliable control from the start.
4Measurement precision
If zone temperature is monitored to identify comfort state, then comfort assessment is achieved, but individual entry into non-comfort zones causes occupant discomfort
Solution Approach 1:
The system uses zone demand signals as an intermediary to assess comfort state without requiring physical entry into zones. By analyzing the demand signals from local controllers, the supervisory controller can determine comfort status remotely, eliminating the harmful effect of sending individuals into potentially uncomfortable environments for assessment purposes.
Data Source
AI summary
A supervisory controller for heating, ventilation, and air conditioning (HVAC) systems is described herein. One device includes a data management module configured to receive a zone demand signal from a local controller of a zone of an HVAC system and receive a number of additional signals from a number of sensors, and a parameter identification module configured to determine, based on the zone demand signal, whether the zone is in a comfort state by loading a best model structure from a number of models and identifying parameters of the best model structure based on data received from the data management module.