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
• Heat transfer modeling
• Artificial intelligence
• Waste heat
• ANFIS