Fault diagnostics of smart buildings (ii) - system modelling and fault detection

Due to the increase in the effects of the energy usage and depletion of resources on the environment, various countries including Singapore are increasingly focused on efforts to improve the energy efficiency of buildings. The efforts embrace the integration of energy efficient technology and resear...

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Bibliographic Details
Main Author: Low, Yan Ting
Other Authors: Hu Guoqiang
Format: Final Year Project (FYP)
Language:English
Published: 2015
Subjects:
Online Access:http://hdl.handle.net/10356/63534
Description
Summary:Due to the increase in the effects of the energy usage and depletion of resources on the environment, various countries including Singapore are increasingly focused on efforts to improve the energy efficiency of buildings. The efforts embrace the integration of energy efficient technology and research on the building services of Singapore. For this study, the research stint is exemplified by the research efforts of SinBerBest, to instill on a cyber-physical test bed conducting energy efficiency developments. In the realm of buildings and industries, it is extensively identified that the air conditioning system takes up the highest energy consumption of about 30% to 50% of a building. Hence the focus of this project will be regarding specifically into the Air Conditioning and Mechanical Ventilation (ACMV) system. The purpose is to model a simulated cyber-physical test bed in TRNSYS software. This software allows components model system of each type of constituents within a building. By understanding the test bed mechanisms, the test bed will be modelled. Parameters for modeling may include that of the building gains, ventilation, and capacity, as well as the whole of the ACMV system. The ACMV system will comprise of the test bed’s water treatment system as well as the air treatment system. The modelled test bed will be monitored and the results analyzed in energy efficiency study. This will be ready for future work of fault detection and diagnosis (FDD) using the simulated model.