Detail publikace

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

POSPÍŠIL, O. MUSIL, P. FUJDIAK, R.

Originální název

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

Typ

článek ve sborníku ve WoS nebo Scopus

Jazyk

angličtina

Originální abstrakt

The 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 (R2 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.

Klíčová slova

microwave link, machine learning, random forest, linear regression, lasso

Autoři

POSPÍŠIL, O.; MUSIL, P.; FUJDIAK, R.

Vydáno

26. 4. 2022

Nakladatel

Brno University of Technology, Faculty of Electrical Engineering and Communication

Místo

Brno

ISBN

978-80-214-6030-0

Kniha

Proceedings II of the 28th Conference STUDENT EEICT 2022 Selected Papers

Edice

1

Strany od

318

Strany do

322

Strany počet

5

URL

https://www.eeict.cz/eeict_download/archiv/sborniky/EEICT_2022_sbornik_2_v3.pdf

BibTex

@inproceedings{BUT178789,
  author="Petr {Musil} and Ondřej {Pospíšil} and Radek {Fujdiak}",
  title="Comparison of machine learning models in outdoor temperature sensing by commercial microwave link",
  booktitle="Proceedings II of the 28th Conference STUDENT EEICT 2022 Selected Papers
",
  year="2022",
  series="1",
  pages="318--322",
  publisher="Brno University of Technology, Faculty of Electrical Engineering and Communication",
  address="Brno",
  isbn="978-80-214-6030-0",
  url="https://www.eeict.cz/eeict_download/archiv/sborniky/EEICT_2022_sbornik_2_v3.pdf"
}