Decentralized HVAC Zone Controller Using Occupancy Feedback
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Solution Overview
Problem
Conventional HVAC control systems face challenges in efficiently optimizing building performance parameters across multiple zones due to computational complexity and centralized control architecture limitations, leading to suboptimal energy savings and occupant comfort.
Innovation Solution
A decentralized control method using a processor-based system that obtains zone environmental data, generates zone cooling load parameters, and determines optimal cool air supply rates through a multi-component cost function, sending set-points to zone controllers for temperature control, facilitating flexible and adaptive control strategies.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Use of energy by moving object
If centralized control architecture with MPC is used to minimize energy consumption across all zones, then energy optimization is improved, but computation complexity increases making it unsuitable for large number of zones
Solution Approach 1:
The patent divides the building into multiple thermal zones, each with its own controller that operates independently. Each zone controller receives occupancy information from its specific zone and adjusts HVAC equipment accordingly, eliminating the need for complex centralized computation while maintaining energy optimization at the zone level.
Solution Approach 2:
The control system tailors HVAC operation to local conditions in each thermal zone by using occupancy information specific to each zone. Each zone controller independently adjusts cooling, heating, and ventilation based on its own occupancy data, providing localized optimization without centralized coordination overhead.
2Ease of operation
If decentralized control methods are used to reduce computation complexity, then ease of operation is improved, but ability to optimize energy consumption across all zones deteriorates
Solution Approach 1:
The system segments control functions into independent zone controllers that each optimize their local zone using simple algorithms based on occupancy data. This segmentation enables easy implementation while maintaining energy efficiency through localized decision-making that responds to actual zone conditions.
Solution Approach 2:
Each thermal zone controller autonomously determines when HVAC equipment should be on or off based on occupancy information from its own zone. The controllers self-regulate without requiring centralized coordination, simplifying implementation while achieving energy savings through occupancy-based control.
3Device complexity
If simple thermostat control is used, then device complexity is reduced, but ability to provide desired indoor environment deteriorates
Solution Approach 1:
The system uses occupancy information as feedback from each thermal zone to automatically adjust HVAC operation. This feedback mechanism enables simple zone controllers to make intelligent decisions about when to turn equipment on or off, maintaining comfortable indoor environments without complex control logic.
Solution Approach 2:
The system proactively adjusts HVAC equipment based on detected occupancy changes before comfort degradation occurs. When occupancy is detected in a zone, the controller pre-conditions the space by turning on cooling or heating, ensuring the desired indoor environment is maintained without requiring complex predictive algorithms.
Data Source
AI summary
There is provided a method of controlling an air-conditioning system associated with a building for optimizing a plurality of building performance parameters in providing an environment with respect to a zone of the building, the method comprising: obtaining zone environmental condition information including zone temperature data associated to the zone, and cooling air temperature data associated to an air handling unit associated to the zone; obtaining, from a zone model generator, zone cooling load parameters associated to the zone with respect to a plurality of time periods and a zone thermal dynamic model; obtaining, from a scheduler, a sequence of optimal cool air supply rates with respect to a plurality of subsequent time periods with respect to the zone determined based on a multi-component cost function including a plurality of components relating to the plurality of building performance parameters; determining, based on the zone thermal dynamic model, a sequence of zone controller set-points corresponding to the sequence of optimal cool air supply rates with respect to the zone using the zone cooling load parameters, the sequence of optimal cool air supply rates, the zone temperature data and the cooling air temperature data associated to the air handling unit; and sending the sequence of zone controller set-points to a zone controller for controlling a temperature of the zone.