A convolutional neural network approach for cable core temperature prediction

Power cable is widely used in the power network, and its significance to the safety and stability of the power network is more and more improving. The high voltage cable is related to the normal production and life of the city, especially in the urban power grid. Because of the particularity of the laying environment, it is very difficult to find the fault points once the cable faults occur, which seriously affects the reliability of the power network. This paper employs convolutional neural networks (CNN) to achieve accurate prediction of cable core temperature. By leveraging multi-dimensional data, the model automatically generates core temperature predictions, offering a reliable foundation for cable operation and maintenance. The results demonstrate that the temperature prediction model exhibits high precision, enabling accurate forecasting of cable core temperature.