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

A. Khosravi; T. Laukkanen; K. Saari; V. Vuorinen

doi:10.1016/j.csite.2021.101613

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

A. Khosravi^*
, T. Laukkanen
, K. Saari
, V. Vuorinen

^*Corresponding author for this work

Department of Energy and Mechanical Engineering

Research output: Contribution to journal › Article › Scientific › peer-review

8 Citations (Scopus)

95 Downloads (Pure)

Abstract

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.

Original language	English
Article number	101613
Number of pages	14
Journal	Case Studies in Thermal Engineering
Volume	28
DOIs	https://doi.org/10.1016/j.csite.2021.101613
Publication status	Published - 1 Nov 2021
MoE publication type	A1 Journal article-refereed

Funding

The authors would like to acknowledge Business Finland for their financial support for the LuxTurrim5G project. We also acknowledge Aalto University , Department of Mechanical Engineering , for the support of the project.

Keywords

5G smart pole
ANFIS
Artificial intelligence
Heat transfer modeling
Waste heat

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ENG_Khosravi_et_al_An_artificial_intelligence_based-model_Case_Studies_in_Thermal_EngineeringFinal published version, 11.3 MBLicence: CC BY

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title = "An artificial intelligence based-model for heat transfer modeling of 5G smart poles",

abstract = "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.",

keywords = "5G smart pole, ANFIS, Artificial intelligence, Heat transfer modeling, Waste heat",

author = "A. Khosravi and T. Laukkanen and K. Saari and V. Vuorinen",

note = "Funding Information: The authors would like to acknowledge Business Finland for their financial support for the LuxTurrim5G project. We also acknowledge Aalto University , Department of Mechanical Engineering , for the support of the project. Publisher Copyright: {\textcopyright} 2021",

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AU - Khosravi, A.

AU - Laukkanen, T.

AU - Saari, K.

AU - Vuorinen, V.

N1 - Funding Information: The authors would like to acknowledge Business Finland for their financial support for the LuxTurrim5G project. We also acknowledge Aalto University , Department of Mechanical Engineering , for the support of the project. Publisher Copyright: © 2021

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N2 - 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.

AB - 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.

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KW - Waste heat

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

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