Please use this identifier to cite or link to this item: https://scholarbank.nus.edu.sg/handle/10635/223514
Title: THE NEXT GENERATION OF BENCHMARKING SYSTEM FOR NUS BUILDINGS - MOVING BEYOND EUI
Authors: SONG, YING
Keywords: Building
PFM
Project and Facilities Management
Clayton Miller
2018/2019 PFM
Energy Benchmarking
Ordinary Least Square
Space Functions
Energy Analysis
Facilities Management
Issue Date: 27-May-2019
Citation: SONG, YING (2019-05-27). THE NEXT GENERATION OF BENCHMARKING SYSTEM FOR NUS BUILDINGS - MOVING BEYOND EUI. ScholarBank@NUS Repository.
Abstract: In Singapore, the building sector consumes approximately 50% of the total energy, which makes it the largest energy consumer among other sectors. (Building and Construction Authority, 2018). According to BCA (2018), the academic sector accounts for 11% of the energy consumed in Singapore, which is the next highest consumer after office buildings. The above facts indicate that the building sector has high potential to help reduce the nation’s energy consumption, hence more measures shall be implemented to help buildings achieve higher energy efficiency. The study aims to create a comprehensive and easily implemented energy benchmarking system for 36 NUS buildings. An ordinary least square method is adopted by incorporating multiple metadata into the regression line especially for data like the breakdown of space functions which is often excluded in the other benchmarking system, and a benchmarking metrics is created from the regression residues, which is calculated by using the actual values divided by the predicted values. Based on the benchmarking metrics, the 36 buildings are ranked into three categories A, B, and C, with A being the most energy efficient buildings and C being the least. Multiple data combinations are explored to generate the best-fitted regression model and achieve the best accuracy. The result of the study can be used by facility managers to conduct a more in-depth analysis of buildings with poor performances, and future renovation plans can be derived from the analysis. The resulted model can also be implemented to larger datasets and benchmarking buildings on a large scale. Keywords: Energy Benchmarking, Ordinary Least Square, Space Functions, Energy Analysis, Facilities Management
URI: https://scholarbank.nus.edu.sg/handle/10635/223514
Appears in Collections:Bachelor's Theses

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