Supervisory controller for HVAC systems
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Solution Overview
Problem
Existing HVAC supervisory controllers face challenges in determining comfort states within building zones due to conflicts with local controllers, are costly and difficult to install, and require additional equipment, leading to increased complexity and dimensionality.
Innovation Solution
A supervisory controller that uses a data management module and parameter identification module to determine comfort states based on zone demand signals, allowing for plug-and-play integration with existing infrastructure, reducing the need for expert installation, and focusing on critical zones with the largest energy demand.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If zone temperature is used to determine comfort state, then the supervisory controller can monitor zone conditions, but conflicts arise with local zone controllers and installation becomes costly and complex
Solution Approach 1:
The patent uses zone demand signals from local controllers as an intermediary indicator to infer comfort state, rather than directly measuring zone temperature. This mediator approach allows the supervisory controller to determine comfort state without direct temperature sensing in each zone, avoiding conflicts with local controllers and reducing installation complexity while maintaining measurement precision for comfort determination.
2Adaptability or versatility
If a supervisory controller is designed to control all local zone controllers in a large multi-zone building, then comprehensive control is achieved, but the dimensionality and complexity of the controller increases significantly
Solution Approach 1:
The patent extracts and focuses control efforts on critical zones with the largest energy demand, rather than attempting to uniformly control all zones. By identifying and prioritizing the most important zones, the system achieves comprehensive adaptability where needed while reducing controller dimensionality and complexity by not managing all zones equally.
Solution Approach 2:
The patent applies different control strategies to different zones based on their specific characteristics and energy demand. Critical zones receive focused supervisory control attention, while other zones rely on local controllers. This local differentiation allows comprehensive coverage without uniformly increasing controller dimensionality across all zones.
3Reliability
If existing supervisory controllers are installed in a large multi-zone building with complex HVAC system, then control functionality is provided, but installation and maintenance become difficult and costly requiring qualified experts
Solution Approach 1:
The patent enables the supervisory controller to automatically identify critical zones and adapt control strategies without requiring expert configuration. The system self-configures by analyzing energy demand data and automatically determining which zones require supervisory control, eliminating the need for qualified experts during installation and maintenance while maintaining reliable control functionality.
Solution Approach 2:
The patent creates a universal supervisory controller that can adapt to different building configurations and HVAC systems without requiring custom installation procedures. The controller universally interfaces with existing local zone controllers and automatically adapts to various building types, making installation and maintenance easier while ensuring reliable control functionality across diverse applications.
Data Source
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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.