Publication Type

Journal Article

Version

publishedVersion

Publication Date

10-2021

Abstract

Over the last decade, collecting massive volumes of data has been made all the more accessible, pushing the building sector to embrace data mining as a powerful tool for harvesting the potential of big data analytics. However repetitive challenges still persist emerging from the need for a common analytical frame, effective application- and insight-driven targeted data selection, as well as benchmarked-supported claims. This study addresses these concerns by putting forward a generic stepwise multidimensional data mining framework tailored to building data, leveraging the dimensional-structures of data cubes. Using the open Building Data Genome Project 2 set, composed of 3053 energy meters from 1636 buildings, we provide an online, open access, implementation illustration of our method applied to automated pattern identification. We define a 3-dimensional building cube echoing typical analytical frames of interest, namely, bottom-up, top-down and temporal drill-in approaches. Our results highlight the importance of application and insight driven mining for effective dimensional-frame targeting. Impactful visualizations were developed allowing practical human inspection, paving the path towards more interpretable analytics.

Keywords

Data mining, Data cube, Generic method, Multidimensional analytics, Machine learning, Building data

Discipline

Energy Policy | Engineering

Research Areas

Integrative Research Areas

Publication

Energy and Buildings

Volume

248

First Page

1

Last Page

16

ISSN

0378-7788

Identifier

10.1016/J.ENBUILD.2021.111195

Publisher

Elsevier

Additional URL

https://doi.org/10.1016/j.enbuild.2021.111195

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