Original title: Comparison of machine learning models in outdoor temperature sensing by commercial microwave link
Authors: Pospisil, Ondrej ; Musil, Petr ; Fujdiak, Radek
Document type: Papers
Language: eng
Publisher: Vysoké učení technické v Brně, Fakulta elektrotechniky a komunikačních technologií
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 (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.
Keywords: microwave link, machine learning, random forest, linear regression, lasso
Host item entry: Proceedings II of the 28st Conference STUDENT EEICT 2022: Selected papers, ISBN 978-80-214-6030-0

Institution: Brno University of Technology (web)
Document availability information: Fulltext is available in the Brno University of Technology Digital Library.
Original record: http://hdl.handle.net/11012/208660

Permalink: http://www.nusl.cz/ntk/nusl-677246


The record appears in these collections:
Universities and colleges > Public universities > Brno University of Technology
Conference materials > Papers