A review of unsupervised statistical learning and visual analytics techniques applied to performance analysis of non-residential buildings

10.1016/j.rser.2017.05.124

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Bibliographic Details
Main Authors:	Miller, Clayton, Nagy, Zoltan, Schlueter, Arno
Other Authors:	DEPT OF BUILDING
Format:	Review
Language:	English
Published:	PERGAMON-ELSEVIER SCIENCE LTD 2021
Subjects:	Green & Sustainable Science & Technology Energy & Fuels Science & Technology - Other Topics Building performance analysis Data mining Unsupervised learning Visual analytics Clustering Novelty detection Smart meter analysis Portfolio analysis Review Building controls and optimization MODEL-PREDICTIVE CONTROL FAULT-DETECTION ARTIFICIAL-INTELLIGENCE CLUSTERING-TECHNIQUES KNOWLEDGE DISCOVERY ENERGY-CONSUMPTION ANOMALY DETECTION LOAD PATTERNS
Online Access:	https://scholarbank.nus.edu.sg/handle/10635/189462
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Institution:	National University of Singapore
Language:	English

id	sg-nus-scholar.10635-189462
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spelling	sg-nus-scholar.10635-1894622023-10-31T21:28:43Z A review of unsupervised statistical learning and visual analytics techniques applied to performance analysis of non-residential buildings Miller, Clayton Nagy, Zoltan Schlueter, Arno DEPT OF BUILDING Green & Sustainable Science & Technology Energy & Fuels Science & Technology - Other Topics Building performance analysis Data mining Unsupervised learning Visual analytics Clustering Novelty detection Smart meter analysis Portfolio analysis Review Building controls and optimization MODEL-PREDICTIVE CONTROL FAULT-DETECTION ARTIFICIAL-INTELLIGENCE CLUSTERING-TECHNIQUES KNOWLEDGE DISCOVERY ENERGY-CONSUMPTION ANOMALY DETECTION LOAD PATTERNS 10.1016/j.rser.2017.05.124 RENEWABLE & SUSTAINABLE ENERGY REVIEWS 81 P1 1365-1377 2021-04-16T06:17:51Z 2021-04-16T06:17:51Z 2018-01-01 2021-04-15T03:23:20Z Review Miller, Clayton, Nagy, Zoltan, Schlueter, Arno (2018-01-01). A review of unsupervised statistical learning and visual analytics techniques applied to performance analysis of non-residential buildings. RENEWABLE & SUSTAINABLE ENERGY REVIEWS 81 (P1) : 1365-1377. ScholarBank@NUS Repository. https://doi.org/10.1016/j.rser.2017.05.124 13640321 18790690 https://scholarbank.nus.edu.sg/handle/10635/189462 en PERGAMON-ELSEVIER SCIENCE 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	Green & Sustainable Science & Technology Energy & Fuels Science & Technology - Other Topics Building performance analysis Data mining Unsupervised learning Visual analytics Clustering Novelty detection Smart meter analysis Portfolio analysis Review Building controls and optimization MODEL-PREDICTIVE CONTROL FAULT-DETECTION ARTIFICIAL-INTELLIGENCE CLUSTERING-TECHNIQUES KNOWLEDGE DISCOVERY ENERGY-CONSUMPTION ANOMALY DETECTION LOAD PATTERNS
spellingShingle	Green & Sustainable Science & Technology Energy & Fuels Science & Technology - Other Topics Building performance analysis Data mining Unsupervised learning Visual analytics Clustering Novelty detection Smart meter analysis Portfolio analysis Review Building controls and optimization MODEL-PREDICTIVE CONTROL FAULT-DETECTION ARTIFICIAL-INTELLIGENCE CLUSTERING-TECHNIQUES KNOWLEDGE DISCOVERY ENERGY-CONSUMPTION ANOMALY DETECTION LOAD PATTERNS Miller, Clayton Nagy, Zoltan Schlueter, Arno A review of unsupervised statistical learning and visual analytics techniques applied to performance analysis of non-residential buildings
description	10.1016/j.rser.2017.05.124
author2	DEPT OF BUILDING
author_facet	DEPT OF BUILDING Miller, Clayton Nagy, Zoltan Schlueter, Arno
format	Review
author	Miller, Clayton Nagy, Zoltan Schlueter, Arno
author_sort	Miller, Clayton
title	A review of unsupervised statistical learning and visual analytics techniques applied to performance analysis of non-residential buildings
title_short	A review of unsupervised statistical learning and visual analytics techniques applied to performance analysis of non-residential buildings
title_full	A review of unsupervised statistical learning and visual analytics techniques applied to performance analysis of non-residential buildings
title_fullStr	A review of unsupervised statistical learning and visual analytics techniques applied to performance analysis of non-residential buildings
title_full_unstemmed	A review of unsupervised statistical learning and visual analytics techniques applied to performance analysis of non-residential buildings
title_sort	review of unsupervised statistical learning and visual analytics techniques applied to performance analysis of non-residential buildings
publisher	PERGAMON-ELSEVIER SCIENCE LTD
publishDate	2021
url	https://scholarbank.nus.edu.sg/handle/10635/189462
_version_	1781792655441657856

A review of unsupervised statistical learning and visual analytics techniques applied to performance analysis of non-residential buildings

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