du.sePublications
Change search
CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • apa
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • chicago-author-date
  • chicago-note-bibliography
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf
A review of data-driven approaches for prediction and classification of building energy consumption
Dalarna University, School of Technology and Business Studies, Energy Technology.ORCID iD: 0000-0002-2369-0169
Show others and affiliations
2018 (English)In: Renewable & sustainable energy reviews, ISSN 1364-0321, E-ISSN 1879-0690, Vol. 82, no 1, p. 1027-1047Article in journal (Refereed) Published
Abstract [en]

A recent surge of interest in building energy consumption has generated a tremendous amount of energy data, which boosts the data-driven algorithms for broad application throughout the building industry. This article reviews the prevailing data-driven approaches used in building energy analysis under different archetypes and granularities, including those methods for prediction (artificial neural networks, support vector machines, statistical regression, decision tree and genetic algorithm) and those methods for classification (K-mean clustering, self-organizing map and hierarchy clustering). The review results demonstrate that the data-driven approaches have well addressed a large variety of building energy related applications, such as load forecasting and prediction, energy pattern profiling, regional energy-consumption mapping, benchmarking for building stocks, global retrofit strategies and guideline making etc. Significantly, this review refines a few key tasks for modification of the data-driven approaches in the context of application to building energy analysis. The conclusions drawn in this review could facilitate future micro-scale changes of energy use for a particular building through the appropriate retrofit and the inclusion of renewable energy technologies. It also paves an avenue to explore potential in macro-scale energy-reduction with consideration of customer demands. All these will be useful to establish a better long-term strategy for urban sustainability.

Place, publisher, year, edition, pages
Elsevier, 2018. Vol. 82, no 1, p. 1027-1047
Keywords [en]
Data driven approach, Building, Energy consumption, Prediction, Classification
National Category
Energy Engineering
Research subject
Energy, Forests and Built Environments
Identifiers
URN: urn:nbn:se:du-26385DOI: 10.1016/j.rser.2017.09.108Scopus ID: 2-s2.0-85030703701OAI: oai:DiVA.org:du-26385DiVA, id: diva2:1147985
Available from: 2017-10-09 Created: 2017-10-09 Last updated: 2017-11-15Bibliographically approved

Open Access in DiVA

No full text in DiVA

Other links

Publisher's full textScopus

Authority records BETA

Zhang, XingxingHan, MengjieZhao, Xiaoyun

Search in DiVA

By author/editor
Zhang, XingxingHan, MengjieZhao, Xiaoyun
By organisation
Energy TechnologyMicrodata Analysis
In the same journal
Renewable & sustainable energy reviews
Energy Engineering

Search outside of DiVA

GoogleGoogle Scholar

doi
urn-nbn

Altmetric score

doi
urn-nbn
Total: 1000 hits
CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • apa
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • chicago-author-date
  • chicago-note-bibliography
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf