Data mining cubes for buildings, a generic framework for multidimensional analytics of building performance data

10.1016/j.enbuild.2021.111195

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Main Authors: Leprince, Julien, Miller, Clayton, Zeiler, Wim
Other Authors: THE BUILT ENVIRONMENT
Format: Article
Language:English
Published: ELSEVIER SCIENCE SA 2022
Subjects:
Online Access:https://scholarbank.nus.edu.sg/handle/10635/229342
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Institution: National University of Singapore
Language: English
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spelling sg-nus-scholar.10635-2293422023-10-31T08:34:32Z Data mining cubes for buildings, a generic framework for multidimensional analytics of building performance data Leprince, Julien Miller, Clayton Zeiler, Wim THE BUILT ENVIRONMENT Science & Technology Technology Construction & Building Technology Energy & Fuels Engineering, Civil Engineering Data mining Data cube Generic method Multidimensional analytics Machine learning Building data MISSING VALUES KNOWLEDGE DISCOVERY FAULT-DETECTION ENERGY DEMAND DATA-DRIVEN ELECTRICITY CONSUMPTION OUTLIER DETECTION IMPUTATION CLASSIFICATION DIAGNOSTICS 10.1016/j.enbuild.2021.111195 ENERGY AND BUILDINGS 248 2022-07-28T05:28:21Z 2022-07-28T05:28:21Z 2021-06-23 2022-07-19T00:49:57Z Article Leprince, Julien, Miller, Clayton, Zeiler, Wim (2021-06-23). Data mining cubes for buildings, a generic framework for multidimensional analytics of building performance data. ENERGY AND BUILDINGS 248. ScholarBank@NUS Repository. https://doi.org/10.1016/j.enbuild.2021.111195 03787788 18726178 https://scholarbank.nus.edu.sg/handle/10635/229342 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
Data mining
Data cube
Generic method
Multidimensional analytics
Machine learning
Building data
MISSING VALUES
KNOWLEDGE DISCOVERY
FAULT-DETECTION
ENERGY DEMAND
DATA-DRIVEN
ELECTRICITY CONSUMPTION
OUTLIER DETECTION
IMPUTATION
CLASSIFICATION
DIAGNOSTICS
spellingShingle Science & Technology
Technology
Construction & Building Technology
Energy & Fuels
Engineering, Civil
Engineering
Data mining
Data cube
Generic method
Multidimensional analytics
Machine learning
Building data
MISSING VALUES
KNOWLEDGE DISCOVERY
FAULT-DETECTION
ENERGY DEMAND
DATA-DRIVEN
ELECTRICITY CONSUMPTION
OUTLIER DETECTION
IMPUTATION
CLASSIFICATION
DIAGNOSTICS
Leprince, Julien
Miller, Clayton
Zeiler, Wim
Data mining cubes for buildings, a generic framework for multidimensional analytics of building performance data
description 10.1016/j.enbuild.2021.111195
author2 THE BUILT ENVIRONMENT
author_facet THE BUILT ENVIRONMENT
Leprince, Julien
Miller, Clayton
Zeiler, Wim
format Article
author Leprince, Julien
Miller, Clayton
Zeiler, Wim
author_sort Leprince, Julien
title Data mining cubes for buildings, a generic framework for multidimensional analytics of building performance data
title_short Data mining cubes for buildings, a generic framework for multidimensional analytics of building performance data
title_full Data mining cubes for buildings, a generic framework for multidimensional analytics of building performance data
title_fullStr Data mining cubes for buildings, a generic framework for multidimensional analytics of building performance data
title_full_unstemmed Data mining cubes for buildings, a generic framework for multidimensional analytics of building performance data
title_sort data mining cubes for buildings, a generic framework for multidimensional analytics of building performance data
publisher ELSEVIER SCIENCE SA
publishDate 2022
url https://scholarbank.nus.edu.sg/handle/10635/229342
_version_ 1781793169570004992