Mining electrical meter data to predict principal building use, performance class, and operations strategy for hundreds of non-residential buildings

10.1016/j.enbuild.2017.09.056

Saved in:

Bibliographic Details
Main Authors:	Miller, Clayton, Meggers, Forrest
Other Authors:	DEPT OF BUILDING
Format:	Article
Language:	English
Published:	ELSEVIER SCIENCE SA 2021
Subjects:	Data mining Building performance Performance classification Energy efficiency Smart meters
Online Access:	https://scholarbank.nus.edu.sg/handle/10635/189463
Tags:	Add Tag No Tags, Be the first to tag this record!
Institution:	National University of Singapore
Language:	English

id	sg-nus-scholar.10635-189463
record_format	dspace
spelling	sg-nus-scholar.10635-1894632023-09-13T21:46:45Z Mining electrical meter data to predict principal building use, performance class, and operations strategy for hundreds of non-residential buildings Miller, Clayton Meggers, Forrest DEPT OF BUILDING Data mining Building performance Performance classification Energy efficiency Smart meters 10.1016/j.enbuild.2017.09.056 ENERGY AND BUILDINGS 156 360-373 2021-04-16T06:28:15Z 2021-04-16T06:28:15Z 2017-12-01 2021-04-15T03:27:05Z Article Miller, Clayton, Meggers, Forrest (2017-12-01). Mining electrical meter data to predict principal building use, performance class, and operations strategy for hundreds of non-residential buildings. ENERGY AND BUILDINGS 156 : 360-373. ScholarBank@NUS Repository. https://doi.org/10.1016/j.enbuild.2017.09.056 03787788 18726178 https://scholarbank.nus.edu.sg/handle/10635/189463 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	Data mining Building performance Performance classification Energy efficiency Smart meters
spellingShingle	Data mining Building performance Performance classification Energy efficiency Smart meters Miller, Clayton Meggers, Forrest Mining electrical meter data to predict principal building use, performance class, and operations strategy for hundreds of non-residential buildings
description	10.1016/j.enbuild.2017.09.056
author2	DEPT OF BUILDING
author_facet	DEPT OF BUILDING Miller, Clayton Meggers, Forrest
format	Article
author	Miller, Clayton Meggers, Forrest
author_sort	Miller, Clayton
title	Mining electrical meter data to predict principal building use, performance class, and operations strategy for hundreds of non-residential buildings
title_short	Mining electrical meter data to predict principal building use, performance class, and operations strategy for hundreds of non-residential buildings
title_full	Mining electrical meter data to predict principal building use, performance class, and operations strategy for hundreds of non-residential buildings
title_fullStr	Mining electrical meter data to predict principal building use, performance class, and operations strategy for hundreds of non-residential buildings
title_full_unstemmed	Mining electrical meter data to predict principal building use, performance class, and operations strategy for hundreds of non-residential buildings
title_sort	mining electrical meter data to predict principal building use, performance class, and operations strategy for hundreds of non-residential buildings
publisher	ELSEVIER SCIENCE SA
publishDate	2021
url	https://scholarbank.nus.edu.sg/handle/10635/189463
_version_	1779153346467201024

Mining electrical meter data to predict principal building use, performance class, and operations strategy for hundreds of non-residential buildings

Similar Items