BEEM: Data-driven building energy benchmarking for Singapore

10.1016/j.enbuild.2022.111869

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Bibliographic Details
Main Authors:	Arjunan, Pandarasamy, Poolla, Kameshwar, Miller, Clayton
Other Authors:	THE BUILT ENVIRONMENT
Format:	Article
Language:	English
Published:	ELSEVIER SCIENCE SA 2022
Subjects:	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
Online Access:	https://scholarbank.nus.edu.sg/handle/10635/229409
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Institution:	National University of Singapore
Language:	English

id	sg-nus-scholar.10635-229409
record_format	dspace
spelling	sg-nus-scholar.10635-2294092023-10-31T20:53:03Z BEEM: Data-driven building energy benchmarking for Singapore Arjunan, Pandarasamy Poolla, Kameshwar Miller, Clayton THE BUILT ENVIRONMENT 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 10.1016/j.enbuild.2022.111869 ENERGY AND BUILDINGS 260 10.1016/j.enbuild.2022.111869 2022-07-29T05:12:35Z 2022-07-29T05:12:35Z 2022-04-01 2022-07-19T00:38:47Z Article 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 03787788 18726178 https://scholarbank.nus.edu.sg/handle/10635/229409 en ELSEVIER SCIENCE SA Elements
institution	National University of Singapore
building	NUS Library
continent	Asia
country	Singapore Singapore
content_provider	NUS Library
collection	ScholarBank@NUS
language	English
topic	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
spellingShingle	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 Arjunan, Pandarasamy Poolla, Kameshwar Miller, Clayton BEEM: Data-driven building energy benchmarking for Singapore
description	10.1016/j.enbuild.2022.111869
author2	THE BUILT ENVIRONMENT
author_facet	THE BUILT ENVIRONMENT Arjunan, Pandarasamy Poolla, Kameshwar Miller, Clayton
format	Article
author	Arjunan, Pandarasamy Poolla, Kameshwar Miller, Clayton
author_sort	Arjunan, Pandarasamy
title	BEEM: Data-driven building energy benchmarking for Singapore
title_short	BEEM: Data-driven building energy benchmarking for Singapore
title_full	BEEM: Data-driven building energy benchmarking for Singapore
title_fullStr	BEEM: Data-driven building energy benchmarking for Singapore
title_full_unstemmed	BEEM: Data-driven building energy benchmarking for Singapore
title_sort	beem: data-driven building energy benchmarking for singapore
publisher	ELSEVIER SCIENCE SA
publishDate	2022
url	https://scholarbank.nus.edu.sg/handle/10635/229409
_version_	1781793173949906944

BEEM: Data-driven building energy benchmarking for Singapore

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