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.

LuxTurrim5G 项目建立在集成不同类型的传感器和设备的基础上，这些传感器和设备已集成到灯杆中，以构建新的数据驱动服务。另一项服务可能是收集电线杆中电气设备产生的废热。在这项研究中，我们开发了一种用于 5G 智能杆传热建模的智能模型。用于构建模型的输入参数是站点纬度（度）、环境温度（$℃$)、内部气流 (m ³ /min) 和时间 (h)。这些输入参数用于预测热流 (W) 和最大板温度 ($℃$) 内的实用程序箱。结果表明，ANFIS-PSO模型为试验数据提供了R值>0.95的准确预测，接近理论最大值1。并且在中午没有检测到内部空气的温度，并且在日出和中午之间以及中午和日落之间使垂直圆柱体的辐射热流最大化。该模型还表明，在北方条件下，产生的热量温度水平超过 30$℃$ 是有限的。