An artificial intelligence based-model for heat transfer modeling of 5G smart poles

The LuxTurrim5G project is built on integrating different types of sensors and equipment that have been integrated into light poles in order to build new data-driven services. One additional service could be to harvest the waste heat produced in the electrical devices in the pole. In this research,...

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Autores principales:	A. Khosravi, T. Laukkanen, K. Saari, V. Vuorinen
Formato:	article
Lenguaje:	EN
Publicado:	Elsevier 2021
Materias:	5G smart pole Heat transfer modeling Artificial intelligence Waste heat ANFIS Engineering (General). Civil engineering (General) TA1-2040
Acceso en línea:	https://doaj.org/article/96146adc7a4a4603b13809362faaaec0
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spelling	oai:doaj.org-article:96146adc7a4a4603b13809362faaaec02021-11-04T04:30:58ZAn artificial intelligence based-model for heat transfer modeling of 5G smart poles2214-157X10.1016/j.csite.2021.101613https://doaj.org/article/96146adc7a4a4603b13809362faaaec02021-12-01T00:00:00Zhttp://www.sciencedirect.com/science/article/pii/S2214157X21007760https://doaj.org/toc/2214-157XThe LuxTurrim5G project is built on integrating different types of sensors and equipment that have been integrated into light poles in order to build new data-driven services. One additional service could be to harvest the waste heat produced in the electrical devices in the pole. In this research, we developed an intelligent model for heat transfer modeling of 5G Smart Poles. The input parameters used to construct the model are latitude of the station (deg), ambient temperature (°C), inside airflow (m3/min) and time (h). These input parameters are employed to predict heat flow (W) and maximum plate temperature (°C) inside the utility box. The results show that the ANFIS-PSO model provides an accurate prediction of R-value >0.95 for the test data, which is close to the maximum theoretically value of 1. The results showed that for the small amount of latitude, the maximum heat flow and temperature of the inside air is not detected at noon and the radiation heat flow to the vertical cylinder is maximized between sunrise and noon as well as between noon and sunset. The model also demonstrated that for the northern conditions, the temperature levels of heat generated over 30 °C are limited.A. KhosraviT. LaukkanenK. SaariV. VuorinenElsevierarticle5G smart poleHeat transfer modelingArtificial intelligenceWaste heatANFISEngineering (General). Civil engineering (General)TA1-2040ENCase Studies in Thermal Engineering, Vol 28, Iss , Pp 101613- (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	5G smart pole Heat transfer modeling Artificial intelligence Waste heat ANFIS Engineering (General). Civil engineering (General) TA1-2040
spellingShingle	5G smart pole Heat transfer modeling Artificial intelligence Waste heat ANFIS Engineering (General). Civil engineering (General) TA1-2040 A. Khosravi T. Laukkanen K. Saari V. Vuorinen An artificial intelligence based-model for heat transfer modeling of 5G smart poles
description	The LuxTurrim5G project is built on integrating different types of sensors and equipment that have been integrated into light poles in order to build new data-driven services. One additional service could be to harvest the waste heat produced in the electrical devices in the pole. In this research, we developed an intelligent model for heat transfer modeling of 5G Smart Poles. The input parameters used to construct the model are latitude of the station (deg), ambient temperature (°C), inside airflow (m3/min) and time (h). These input parameters are employed to predict heat flow (W) and maximum plate temperature (°C) inside the utility box. The results show that the ANFIS-PSO model provides an accurate prediction of R-value >0.95 for the test data, which is close to the maximum theoretically value of 1. The results showed that for the small amount of latitude, the maximum heat flow and temperature of the inside air is not detected at noon and the radiation heat flow to the vertical cylinder is maximized between sunrise and noon as well as between noon and sunset. The model also demonstrated that for the northern conditions, the temperature levels of heat generated over 30 °C are limited.
format	article
author	A. Khosravi T. Laukkanen K. Saari V. Vuorinen
author_facet	A. Khosravi T. Laukkanen K. Saari V. Vuorinen
author_sort	A. Khosravi
title	An artificial intelligence based-model for heat transfer modeling of 5G smart poles
title_short	An artificial intelligence based-model for heat transfer modeling of 5G smart poles
title_full	An artificial intelligence based-model for heat transfer modeling of 5G smart poles
title_fullStr	An artificial intelligence based-model for heat transfer modeling of 5G smart poles
title_full_unstemmed	An artificial intelligence based-model for heat transfer modeling of 5G smart poles
title_sort	artificial intelligence based-model for heat transfer modeling of 5g smart poles
publisher	Elsevier
publishDate	2021
url	https://doaj.org/article/96146adc7a4a4603b13809362faaaec0
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An artificial intelligence based-model for heat transfer modeling of 5G smart poles

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