Please use this identifier to cite or link to this item: https://doi.org/10.1016/j.enbuild.2022.111869
Title: BEEM: Data-driven building energy benchmarking for Singapore
Authors: Arjunan, Pandarasamy
Poolla, Kameshwar
Miller, Clayton 
Keywords: Science & Technology
Technology
Construction & Building Technology
Energy & Fuels
Engineering, Civil
Engineering
Building energy benchmarking
Building energy labeling
Regression analysis
Gradient boosting trees
Feature interaction
Interpretable machine learning
PERFORMANCE BENCHMARKING
OFFICE BUILDINGS
CONSUMPTION
CLASSIFICATION
METHODOLOGY
PREDICTION
EXAMPLE
MODEL
Issue Date: 1-Apr-2022
Publisher: ELSEVIER SCIENCE SA
Citation: Arjunan, Pandarasamy, Poolla, Kameshwar, Miller, Clayton (2022-04-01). BEEM: Data-driven building energy benchmarking for Singapore. ENERGY AND BUILDINGS 260 : 10.1016/j.enbuild.2022.111869. ScholarBank@NUS Repository. https://doi.org/10.1016/j.enbuild.2022.111869
Abstract: Building energy use benchmarking is the process of measuring the energy performance of buildings relative to their peer group for creating awareness and identifying energy-saving opportunities. In this paper, we present the design and implementation of BEEM, a data-driven energy use benchmarking system for buildings in Singapore. The peer groups for comparison are established using a public energy disclosure data set. We use an ensemble tree algorithm for accurately modeling building energy use and for identifying the most influential factors. Our models reduce the prediction error from 24.39% to 6.04%, on average, when compared to the baseline linear regression models, which were used in the previous energy efficiency labeling program in Singapore, and outperforms ten other recent models. Using the prototype implementation of BEEM, we benchmarked three building types, office (290), hotel (203), and retail (125), and compared their rating. The code repository and the accompanying data set are released as an open-source project for community use.
Source Title: ENERGY AND BUILDINGS
URI: https://scholarbank.nus.edu.sg/handle/10635/229409
ISSN: 03787788
18726178
DOI: 10.1016/j.enbuild.2022.111869
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