Please use this identifier to cite or link to this item: https://scholarbank.nus.edu.sg/handle/10635/222879
Title: A STUDY OF INDOOR ENVIRONMENTAL QUALITY AND OCCUPANCY USING MULTI-VARIABLE SENSORS TO BENEFIT FACILITY MANAGEMENT
Authors: DUNCAN FRANCIS TANINDRA
Keywords: facility management
indoor environmental quality
occupancy
productivity
sensor
internet of things
Building
PFM
Project and Facilities Management
2018/2019 PFM
Sekhar Kondepudi
Issue Date: 21-Dec-2018
Citation: DUNCAN FRANCIS TANINDRA (2018-12-21). A STUDY OF INDOOR ENVIRONMENTAL QUALITY AND OCCUPANCY USING MULTI-VARIABLE SENSORS TO BENEFIT FACILITY MANAGEMENT. ScholarBank@NUS Repository.
Abstract: Facility management exists in every building and it plays an important role to ensure that the building remains at optimal working conditions. This provides a space that has a comfortable level of indoor environmental quality which allows building occupants to work productively. There are many variables affecting indoor environmental quality and some can be difficult to monitor, but sensors are able to detect many of these variables and can aid facility management in measuring them. They are also able to detect these variables across the entire facility simultaneously which is something impossible traditionally. While sensor deployment has its limitations such as cost and compatibility, its potential benefits should not be overlooked. Thus, this study deployed multi-variable sensors in an office building in Bangalore, India as a network of internet of things to better understand its indoor environmental quality. The sensors measure temperature, relative humidity, light, noise, carbon dioxide and total volatile organic compounds. It seeks to monitor the building through sensor data collection and identify trends, anomalies and correlations among the measured variables which proved to be able to detect occupancy patterns. In conclusion, the discoveries made by the sensor deployment identified certain areas in the building to be performing poorly for indoor environmental quality and recommendations were given to the facility manager to improve them. The results of the study shows promise in using potential data analysis and processing of big data from data collected through multi-variable sensors.
URI: https://scholarbank.nus.edu.sg/handle/10635/222879
Appears in Collections:Bachelor's Theses

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