From Electricity and Water Consumption Data to Information on Office Occupancy: A Supervised and Unsupervised Data Mining Approach

dc.contributorAalto-yliopistofi
dc.contributorAalto Universityen
dc.contributor.authorStjelja, Davoren_US
dc.contributor.authorJokisalo, Juhaen_US
dc.contributor.authorKosonen, Ristoen_US
dc.contributor.departmentDepartment of Mechanical Engineeringen
dc.contributor.groupauthorEnergy efficiency and systemsen
dc.date.accessioned2020-12-31T08:48:20Z
dc.date.available2020-12-31T08:48:20Z
dc.date.issued2020-12-02en_US
dc.description.abstractClimate change and technological development are pushing buildings to become more sophisticated. The installation of modern building automation systems, smart meters, and IoT devices is increasing the amount of available building operational data. The common term for this kind of building is a smart building but producing large amounts of raw data does not automatically offer intelligence that would offer new insights to the building’s operation. Smart meters are mainly used only for tracking the energy or water consumption in the building. On the other hand, building occupancy is usually not monitored in the building at all, even though it is one of the main influencing factors of consumption and indoor climate parameters. This paper is bringing the true smart building closer to practice by using machine learning methods with sub-metered electricity and water consumptions to predict the building occupancy. In the first approach, the number of occupants was predicted in an office floor using a supervised data mining method Random Forest. The model performed the best with the use of all predictors available, while from individual predictors, the sub-metered electricity used for office equipment showed the best performance. Since the supervised approach requires the continuous long-term collection of ground truth reference data (between one to three months, by this study), an unsupervised data mining method k-means clustering was tested in the second approach. With the unsupervised method, this study was able to predict the level of occupancy in a day as zero, medium, or high in a case study office floor using the equipment electricity consumption.en
dc.description.versionPeer revieweden
dc.format.extent23
dc.format.mimetypeapplication/pdfen_US
dc.identifier.citationStjelja, D, Jokisalo, J & Kosonen, R 2020, ' From Electricity and Water Consumption Data to Information on Office Occupancy: A Supervised and Unsupervised Data Mining Approach ', Applied Sciences (Switzerland), vol. 10, no. 24, 9089, pp. 1-23 . https://doi.org/10.3390/app10249089en
dc.identifier.doi10.3390/app10249089en_US
dc.identifier.issn2076-3417
dc.identifier.otherPURE UUID: c9d3ed7b-378a-4473-b810-718f722a3e2aen_US
dc.identifier.otherPURE ITEMURL: https://research.aalto.fi/en/publications/c9d3ed7b-378a-4473-b810-718f722a3e2aen_US
dc.identifier.otherPURE LINK: http://www.scopus.com/inward/record.url?scp=85098096114&partnerID=8YFLogxKen_US
dc.identifier.otherPURE FILEURL: https://research.aalto.fi/files/54202071/Applied_Sciences_2020_.pdfen_US
dc.identifier.urihttps://aaltodoc.aalto.fi/handle/123456789/101624
dc.identifier.urnURN:NBN:fi:aalto-2020123160445
dc.language.isoenen
dc.publisherSpringer International Publishing AG
dc.relation.ispartofseriesApplied Sciences (Switzerland)en
dc.rightsopenAccessen
dc.subject.keywordoccupancy predictionen_US
dc.subject.keywordsmart meteren_US
dc.subject.keywordmachine learningen_US
dc.subject.keywordcluster analysisen_US
dc.subject.keyworddata-drivenen_US
dc.subject.keywordsmart buildingen_US
dc.titleFrom Electricity and Water Consumption Data to Information on Office Occupancy: A Supervised and Unsupervised Data Mining Approachen
dc.typeA1 Alkuperäisartikkeli tieteellisessä aikakauslehdessäfi
dc.type.versionpublishedVersion
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