Design and implementation of an intelligent building automation system

Abstract

Intelligent Building Automation System (IBAS) has the ability to monitor and control various facilities within the building so as to offer its users or occupants with effective security, improved productivity, human comfort, and efficient energy management. The main IBAS subsystems include Heat, Ventilation and Air Conditioning (HVAC), Lighting Systems, Life and Safety System, and Access Control. This thesis focuses on the design implementation and development of HVAC and lighting controllers. Currently, most HVAC systems are controlled using conventional controller which relies on process mathematical model. However, this type of controller is not suitable for system whose operating environment is nonlinear as in the case of HVAC. The introduction of lighting in the place where HVAC operates is a parameter that needs careful attention as it could influence the HVAC performance. To address this problem, this thesis proposes fuzzy logic controller with tuning mechanism via output scaling-factor method for HVAC system. The developed HVAC controller is specifically applied to Air Handling Unit (AHU) upstream process where mixing dampers are modulated to provide a lowest enthalpy of airstream entering AHU downstream process. This method is expected to improve HVAC performance that is measured in terms of thermal comfort level in the space it serves and the energy it consumes. The main function of HVAC is to overcome thermal load in the space it serves in order to provide desirable comfort to space occupants. This thermal load comes in two ways of heat transfer such as convection and radiation. Space lighting is a common source of heat transfer by radiation and therefore its intensity must be minimized to an acceptable level to reduce its effect on HVAC performance. This thesis includes the design of fuzzy logic controller for lighting systems. Since HVAC and lighting systems exhibit nonlinear processes, intelligent control technique such as fuzzy logic controller is the best option. The proposed control architectures are developed and implemented by using National Instrument LabVIEW and executed using FieldPoint hardware. Experimental results show that HVAC performance can be improved by properly regulating the AHU mixing dampers and fans using fuzzy controller with scaling-factor tuning procedure. Similarly, based on the experimental result, lighting system control performance has been proven to be satisfactory with the use of light intensity detector and fuzzy logic control system. Consequently, fuzzy controller exhibits good performance for nonlinear process, especially in the case of HVAC and lighting systems.