Comparison of machine learning models in outdoor temperature sensing by commercial microwave link

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but.event.date26.04.2022cs
but.event.titleSTUDENT EEICT 2022cs
dc.contributor.authorPospisil, Ondrej
dc.contributor.authorMusil, Petr
dc.contributor.authorFujdiak, Radek
dc.date.accessioned2022-12-06T13:22:00Z
dc.date.available2022-12-06T13:22:00Z
dc.date.issued2022cs
dc.description.abstractThe main objective of this work is to focus on outdoor temperature prediction using machine learning based on parameters from commercial microwave links. This information can be used to refine the weather information at a given link location. Three machine learning models (random forest, linear regression, and lasso) are used for prediction using a combination of two datasets (ERA5 weather dataset and CML monitoring database dataset). The results were evaluated based on two evaluation metrics (R^2 and mean absolute error (MAE)). In this work, the ERA5 outdoor temperature was found to be correlated with the temperature of the microwave link unit, and results were obtained with an accuracy of 0.87144 based on the MAE metric. Thus, the results can fairly well predict actual outdoor temperatures in the microwave link area based on the microwave link unit temperature.en
dc.formattextcs
dc.format.extent318-322cs
dc.format.mimetypeapplication/pdfen
dc.identifier.citationProceedings II of the 28st Conference STUDENT EEICT 2022: Selected papers. s. 318-322. ISBN 978-80-214-6030-0cs
dc.identifier.doi10.13164/eeict.2022.318
dc.identifier.isbn978-80-214-6030-0
dc.identifier.urihttp://hdl.handle.net/11012/208660
dc.language.isoencs
dc.publisherVysoké učení technické v Brně, Fakulta elektrotechniky a komunikačních technologiícs
dc.relation.ispartofProceedings II of the 28st Conference STUDENT EEICT 2022: Selected papersen
dc.relation.urihttps://conf.feec.vutbr.cz/eeict/index/pages/view/ke_stazenics
dc.rights© Vysoké učení technické v Brně, Fakulta elektrotechniky a komunikačních technologiícs
dc.rights.accessopenAccessen
dc.subjectmicrowave link, machine learning, random forest, linear regression, lassoen
dc.titleComparison of machine learning models in outdoor temperature sensing by commercial microwave linken
dc.type.driverconferenceObjecten
dc.type.statusPeer-revieweden
dc.type.versionpublishedVersionen
eprints.affiliatedInstitution.departmentFakulta elektrotechniky a komunikačních technologiícs
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