Advanced fuzzy logic based control systems for an institutional building in subtropical climate

Building management systems (BMS) have the ability to monitor and control buildings mechanical and electrical systems, such as heating, ventilation and air conditioning (HVAC) and lighting systems, for providing indoor thermal comfort and reducing energy consumption. However, most HVAC systems are controlled using conventional controller the functions of which are based on ON/OFFs controller and Proportional-Integral-Derivative (PID) controllers. These controllers are not efficient at saving energy because of the operations of HVAC systems are nonlinear. Thus, the implementation of fuzzy-logic-based control systems within smart buildings are necessary as they are more efficient and will consequently reduce building energy consumption as well as negative impacts on environment. The main aim of this study was to design and develop an advanced fuzzy-logic-based controller for HVAC and indoor lighting systems for an institutional building in subtropical Central Queensland (Australia) to assess its energy and environmental performances, and compare these with the performances of conventional ON/OFF and PID controllers. The fuzzy-logic-based model and control strategies were designed and developed to control indoor temperature, humidity, air quality, air velocity, daylight integration, thermal comfort and energy balance. In addition, the model for indoor temperature and humidity transfer matrix, uncertainties of users’ comfort preference set-points and a fuzzy algorithm were developed. The performances of both ON/OFF and PID control system, and proposed fuzzy-logic-based control systems were simulated using MATLAB software. DAYSIM software was used to simulate the illuminance of lighting system. DesignBuilder and EnergyPlus software were used to develop case study building layout and thermal performance modelling. The simulation was done for indoor and outdoor temperature and humidity control, indoor air quality, and illuminance control. The simulated results were analysed on the basis of real-life events such as the usage of ambient air when its temperature and humidity matches indoor thermal comfort set-point, the usage of existing daylighting rather than the usage of electric lighting, and the consideration of the building’s occupancy level taking into account the controllers’ execution performance panel containing response speed, overshot and robustness adaptability. It was found that an energy savings of about 10% can be achieved if fuzzy-logic-based controllers are introduced compared to conventional PID controllers at full occupancy level for the case study building’s HVAC and lighting systems. The simulation was also done for 50% occupancy and 25% occupancy levels which indicated an energy savings of about 14% at 50% occupancy level, and 24% at 25% occupancy level compared to full occupancy at a given time. In addition, life cycle costs savings of about 20.5% can be achieved using the proposed fuzzy-logic controller. The systems payback period is expected to be nine years, and the system is able to reduce greenhouse gas emissions of 25.5 tonnes of CO2 per annum from the case study building. The thesis has contributed to the process development and design of advanced fuzzy logic controllers for smart buildings in subtropical climate of Australia which is a successful alternative to conventional control systems especially where indoor air quality and mould growth issue is a big concern, e.g. in hospitals, libraries and museums. The novelty of this work is the development of an energy efficient and environment friendly control of HVAC and lighting systems using real life and time events such as ambient air, day-light and actual occupancy levels which have not been addressed previously within an Australian institutional building, specifically under the subtropical climate conditions. Thus, the outcomes of the study will provide designers, developers and decision makers with the essential information and knowledge of applications of advanced fuzzy logic control system for smart buildings