Please use this identifier to cite or link to this item: https://scholarbank.nus.edu.sg/handle/10635/221586
DC FieldValue
dc.titleENERGY PERFORMANCE EVALUATION OF ACADEMIC BUILDINGS THROUGH DATA DRIVEN ANALYTICS
dc.contributor.authorGANDHI, PUNIT VARSHESH
dc.date.accessioned2018-12-06T06:44:32Z
dc.date.accessioned2022-04-22T17:42:43Z
dc.date.available2019-09-26T14:14:01Z
dc.date.available2022-04-22T17:42:43Z
dc.date.issued2018-12-06
dc.identifier.citationGANDHI, PUNIT VARSHESH (2018-12-06). ENERGY PERFORMANCE EVALUATION OF ACADEMIC BUILDINGS THROUGH DATA DRIVEN ANALYTICS. ScholarBank@NUS Repository.
dc.identifier.urihttps://scholarbank.nus.edu.sg/handle/10635/221586
dc.description.abstractThere has been a recent high emphasis on building energy efficiency and conversation around the world. While the Building and Construction Authority (BCA) in Singapore has sufficiently promoted green buildings and energy efficient systems, there is still a lack of clear and effective controls to monitor and manage these systems specific to the demands of a building. Occupants and their behaviour are one of the key influencers of building energy consumption. Hence, this study focuses on the development of an analytical approach to understanding the relationship between occupancy patterns and energy consumption; specifically in the operations of the building air conditioning system. The methodology adopted for this study includes an original approach comprising of occupancy and energy consumption factors, average profiles, scatter plots and pivot tables for a clearer portrayal of the data obtained from the case study building. The main opportunity for improvement identified from the analysis was the large variation in energy consumed by the air conditioning system at similar timings across different days despite the consistent occupancy patterns and outdoor air temperature profiles. This study proposes two novel and innovative concepts through the integration of active controls into the building’s air conditioning system. The first concept involves making accurate occupancy predictions based on real time measurements and historical records. The second introduces statistical benchmarking of the air conditioning system’s consumption. Various scenarios have been presented and their potential energy savings quantified in this study. Several recommendations have been identified to improve the data’s accuracy and provide propositions for future studies. It is notable, however, that there are various other factors which contribute to a building’s energy consumption but are not considered in this study.
dc.language.isoen
dc.sourcehttps://lib.sde.nus.edu.sg/dspace/handle/sde/4370
dc.subjectEnergy
dc.subjectAir Conditioning
dc.subjectData Analytics
dc.subjectBenchmarking
dc.subjectBuilding
dc.subjectPFM
dc.subjectProject and Facilities Management
dc.subjectLee Siew Eang
dc.subject2018/2019 PFM
dc.typeDissertation
dc.contributor.departmentBUILDING
dc.contributor.supervisorLEE SIEW EANG
dc.description.degreeBachelor's
dc.description.degreeconferredBACHELOR OF SCIENCE (PROJECT AND FACILITIES MANAGEMENT)
dc.embargo.terms2019-01-07
Appears in Collections:Bachelor's Theses

Show simple item record
Files in This Item:
File Description SizeFormatAccess SettingsVersion 
Punit Varshesh Gandhi 2018-2019.pdfEnergy Performance Evaluation of Academic Buildings through Data Driven Analytics3.81 MBAdobe PDF

RESTRICTED

NoneLog In

Page view(s)

24
checked on Jan 26, 2023

Download(s)

19
checked on Jan 26, 2023

Google ScholarTM

Check


Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.