IoT-based platform for automated IEQ spatio-temporal analysis in buildings using machine learning techniques

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dc.contributor.authorTroncoso-Pastoriza, Francisco
dc.contributor.authorMartínez-Comesaña, Miguel
dc.contributor.authorOgando-Martínez, Ana
dc.contributor.authorLópez-Gómez, Javier
dc.contributor.authorEguía-Oller, Pablo
dc.contributor.authorFebrero-Garrido, Lara
dc.date.accessioned2025-01-24T09:04:49Z
dc.date.available2025-01-24T09:04:49Z
dc.date.issued2022-07
dc.description.abstractProviding accurate information about the indoor environmental quality (IEQ) conditions inside building spaces is essential to assess the comfort levels of their occupants. These values may vary inside the same space, especially for large zones, requiring many sensors to produce a fine-grained representation of the space conditions, which increases hardware installation and maintenance costs. However, sound interpolation techniques may produce accurate values with fewer input points, reducing the number of sensors needed. This work presents a platform to automate this accurate IEQ representation based on a few sensor devices placed across a large building space. A case study is presented in a research centre in Spain using 8 wall-mounted devices and an additional moving device to train a machine learning model. The system yields accurate results for estimations at positions and times never seen before by the trained model, with relative errors between 4% and 10% for the analysed variables.
dc.identifier.citationAutomation in Construction Volume 139, July 2022, 104261
dc.identifier.issn1872-7891
dc.identifier.other10.1016/j.autcon.2022.104261
dc.identifier.urihttp://calderon.cud.uvigo.es/handle/123456789/870
dc.language.isoen
dc.publisherAutomation in Construction
dc.titleIoT-based platform for automated IEQ spatio-temporal analysis in buildings using machine learning techniques
dc.typeArticle
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