Umeå University's logo

umu.sePublications
Change search
CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • apa
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • 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
Toward Delicate Anomaly Detection of Energy Consumption for Buildings: Enhance the Performance From Two Levels
Umeå University, Faculty of Science and Technology, Department of Chemistry.ORCID iD: 0000-0002-0346-3081
Mestro AB, Stockholm, Sweden.
Umeå University, Faculty of Science and Technology, Department of Chemistry.ORCID iD: 0000-0003-3799-6094
Umeå University, Faculty of Science and Technology, Department of Chemistry.ORCID iD: 0000-0001-8709-6970
Show others and affiliations
2022 (English)In: IEEE Access, E-ISSN 2169-3536, Vol. 10, p. 31649-31659Article in journal (Refereed) Published
Abstract [en]

Buildings are highly energy-consuming and therefore are largely accountable for environmental degradation. Detecting anomalous energy consumption is one of the effective ways to reduce energy consumption. Besides, it can contribute to the safety and robustness of building systems since anomalies in the energy data are usually the reflection of malfunctions in building systems. As the most flexible and applicable type of anomaly detection approach, unsupervised anomaly detection has been implemented in several studies for building energy data. However, no studies have investigated the joint influence of data structures and algorithms’ mechanisms on the performance of unsupervised anomaly detection for building energy data. Thus, we put forward a novel workflow based on two levels, data structure level and algorithm mechanism level, to effectively detect the imperceptible anomalies in the energy consumption profiles of buildings. The proposed workflow was implemented in a case study for identifying the anomalies in three real-world energy consumption datasets from two types of commercial buildings. Two aims were achieved through the case study. First, it precisely detected the contextual anomalies concealed beneath the time variation of the energy consumption profiles of the three buildings. The performance in terms of areas under the precision-recall curves (AUC_PR) for the three given datasets were 0.989, 0.941, and 0.957, respectively. Second, more broadly, the joint effect of the two levels was examined. On the data level, all four detectors on the contextualized data were superior to their counterparts on the original data. On the algorithm level, there was a consistent ranking of detectors regarding their detecting performances on the contextualized data. The consistent ranking suggests that local approaches outperform global approaches in the scenarios where the goal is to detect the instances deviating from their contextual neighbors rather than the rest of the entire data.

Place, publisher, year, edition, pages
IEEE, 2022. Vol. 10, p. 31649-31659
Keywords [en]
Buildings, energy consumption, anomaly detection, contextualization, unsupervised learning
National Category
Computer Sciences
Identifiers
URN: urn:nbn:se:umu:diva-193342DOI: 10.1109/ACCESS.2022.3160170ISI: 000773226800001Scopus ID: 2-s2.0-85126531395OAI: oai:DiVA.org:umu-193342DiVA, id: diva2:1647536
Available from: 2022-03-28 Created: 2022-03-28 Last updated: 2023-09-05Bibliographically approved

Open Access in DiVA

fulltext(816 kB)111 downloads
File information
File name FULLTEXT01.pdfFile size 816 kBChecksum SHA-512
242d0701337f6168e242a8598a290196306c0f312f1e95f8a781af3445defa1ee84ce641d14b2900caf0e6221d1e37192a8627c78e220c3db10e5c81112eacb3
Type fulltextMimetype application/pdf

Other links

Publisher's full textScopus

Authority records

Wang, DongTrygg, JohanTysklind, MatsJiang, Lili

Search in DiVA

By author/editor
Wang, DongTrygg, JohanTysklind, MatsJiang, Lili
By organisation
Department of ChemistryDepartment of Computing Science
In the same journal
IEEE Access
Computer Sciences

Search outside of DiVA

GoogleGoogle Scholar
Total: 111 downloads
The number of downloads is the sum of all downloads of full texts. It may include eg previous versions that are now no longer available

doi
urn-nbn

Altmetric score

doi
urn-nbn
Total: 245 hits
CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • apa
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • 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