A Multi-agent Decision Process for Controlling Heating, Ventilation, and Air-conditioning Systems

Open Access
Author:
Windham, Andrew Winsome
Graduate Program:
Architectural Engineering
Degree:
Doctor of Philosophy
Document Type:
Dissertation
Date of Defense:
March 04, 2014
Committee Members:
  • Stephen James Treado, Dissertation Advisor
  • Stephen James Treado, Committee Chair
  • James Freihaut, Committee Member
  • John Messner, Committee Member
  • Soundar Kumara, Committee Member
Keywords:
  • Building Automation
  • Multi-Agent Systems
  • Conditional Game Theory
  • HVAC
  • Probabilistic Graphical Models
  • Distributed Control
Abstract:
The primary focus of this work is to explore and describe a proposed decision process for use in controlling heating, ventilation, and air conditioning systems in buildings. The larger problem that houses the decision process is structured as a multi-agent building control system. This is because multi-agent systems are an effective way to incorporate new sources of external and sporadic information into the basic system. In this study, sources of occupant information are used to demonstrate the decision process. The decision process begins with agents formulating context specific opinions using a method that is derived from Bayesian Networks and probabilistic graphical modeling. From there, agents convey their preferences using conditional game theory, setting up the group of agents for action. While there are a variety of action procedures that could be used, the process was developed with Nash equilibriums in mind. The Nash equilibriums, however, were not yet successfully implemented due to the early stages of the social influence modeling. Instead, a basic social welfare function was employed to demonstrate the decision process for a setpoint decision context. A three zone, variable air volume simulator was developed in MATLAB to represent a physical environment and to act as a reference case for typical PI control. The simulator is then used for exploring and describing the decision process implementation, which is run in parallel to the basic system simulator. The Bayesian Network-based opinion formulation method developed by this work holds promise and has been found to merit further development. A component of the conditional game structure, rejectability, was not found to be a natural context for use in the setpoint decision context, but it may be improved upon. Overall, systems integration was desired and appears possible by implementing the decision process.