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

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216305%3A26220%2F22%3APU145247" target="_blank" >RIV/00216305:26220/22:PU145247 - isvavai.cz</a>

Výsledek na webu

<a href="https://www.eeict.cz/eeict_download/archiv/sborniky/EEICT_2022_sbornik_2_v3.pdf" target="_blank" >https://www.eeict.cz/eeict_download/archiv/sborniky/EEICT_2022_sbornik_2_v3.pdf</a>

DOI - Digital Object Identifier

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Jazyk výsledku

angličtina

Název v původním jazyce

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

Popis výsledku v původním jazyce

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.

Název v anglickém jazyce

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

Popis výsledku anglicky

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.

Druh

D - Stať ve sborníku

CEP obor

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OECD FORD obor

20203 - Telecommunications

Projekt

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Návaznosti

S - Specificky vyzkum na vysokych skolach

Rok uplatnění

2022

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Název statě ve sborníku

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

ISBN

978-80-214-6030-0

ISSN

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e-ISSN

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Počet stran výsledku

5

Strana od-do

318-322

Název nakladatele

Brno University of Technology, Faculty of Electrical Engineering and Communication

Místo vydání

Brno

Místo konání akce

Brno

Datum konání akce

26. 4. 2022

Typ akce podle státní příslušnosti

WRD - Celosvětová akce

Kód UT WoS článku

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