Publication detail

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

POSPÍŠIL, O. MUSIL, P. FUJDIAK, R.

Original Title

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

Type

conference paper

Language

English

Original Abstract

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.

Keywords

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

Authors

POSPÍŠIL, O.; MUSIL, P.; FUJDIAK, R.

Released

26. 4. 2022

Publisher

Brno University of Technology, Faculty of Electrical Engineering and Communication

Location

Brno

ISBN

978-80-214-6030-0

Book

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

Edition

1

Pages from

318

Pages to

322

Pages count

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"
}