Using Google Trends as a proxy for occupant behavior to predict building

10.1016/j.apenergy.2021.118343

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Bibliographic Details
Main Authors:	Fu, Chun, Miller, Clayton
Other Authors:	THE BUILT ENVIRONMENT
Format:	Article
Language:	English
Published:	ELSEVIER SCI LTD 2022
Subjects:	Science & Technology Technology Energy & Fuels Engineering, Chemical Engineering Google Trends Machine learning Kaggle competition Model error reduction Building energy prediction Energy model ENERGY-CONSUMPTION COOLING LOAD SIMULATION MACHINE
Online Access:	https://scholarbank.nus.edu.sg/handle/10635/229410
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Institution:	National University of Singapore
Language:	English

id	sg-nus-scholar.10635-229410
record_format	dspace
spelling	sg-nus-scholar.10635-2294102023-10-31T20:52:56Z Using Google Trends as a proxy for occupant behavior to predict building Fu, Chun Miller, Clayton THE BUILT ENVIRONMENT Science & Technology Technology Energy & Fuels Engineering, Chemical Engineering Google Trends Machine learning Kaggle competition Model error reduction Building energy prediction Energy model ENERGY-CONSUMPTION COOLING LOAD SIMULATION MACHINE 10.1016/j.apenergy.2021.118343 APPLIED ENERGY 310 10.1016/j.apenergy.2021.118343 2022-07-29T05:18:56Z 2022-07-29T05:18:56Z 2022-03-15 2022-07-19T00:41:36Z Article Fu, Chun, Miller, Clayton (2022-03-15). Using Google Trends as a proxy for occupant behavior to predict building. APPLIED ENERGY 310 : 10.1016/j.apenergy.2021.118343. ScholarBank@NUS Repository. https://doi.org/10.1016/j.apenergy.2021.118343 03062619 18729118 https://scholarbank.nus.edu.sg/handle/10635/229410 en ELSEVIER SCI LTD 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 Energy & Fuels Engineering, Chemical Engineering Google Trends Machine learning Kaggle competition Model error reduction Building energy prediction Energy model ENERGY-CONSUMPTION COOLING LOAD SIMULATION MACHINE
spellingShingle	Science & Technology Technology Energy & Fuels Engineering, Chemical Engineering Google Trends Machine learning Kaggle competition Model error reduction Building energy prediction Energy model ENERGY-CONSUMPTION COOLING LOAD SIMULATION MACHINE Fu, Chun Miller, Clayton Using Google Trends as a proxy for occupant behavior to predict building
description	10.1016/j.apenergy.2021.118343
author2	THE BUILT ENVIRONMENT
author_facet	THE BUILT ENVIRONMENT Fu, Chun Miller, Clayton
format	Article
author	Fu, Chun Miller, Clayton
author_sort	Fu, Chun
title	Using Google Trends as a proxy for occupant behavior to predict building
title_short	Using Google Trends as a proxy for occupant behavior to predict building
title_full	Using Google Trends as a proxy for occupant behavior to predict building
title_fullStr	Using Google Trends as a proxy for occupant behavior to predict building
title_full_unstemmed	Using Google Trends as a proxy for occupant behavior to predict building
title_sort	using google trends as a proxy for occupant behavior to predict building
publisher	ELSEVIER SCI LTD
publishDate	2022
url	https://scholarbank.nus.edu.sg/handle/10635/229410
_version_	1781793174301179904

Using Google Trends as a proxy for occupant behavior to predict building

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