Cable Temperature Prediction Algorithm Based on the MSST-Net

To improve the accuracy of cable temperature anomaly prediction and ensure power supply reliability, this paper proposes a multi-scale spatiotemporal model called MSST-Net, addressing the multi-scale temporal characteristics and spatial correlations of cable temperature data. Based on the monthly periodicity of cable temperature data, we preprocessed monitoring data from the KN1 and KN2 sections of Guangzhou's underground utility tunnel from 2023 to 2024: using the Isolation Forest algorithm to remove outliers, applying Min-Max normalization to eliminate dimensional differences, and selecting five key features including current load, voltage, and ambient temperature using Spearman's correlation coefficient. Subsequently, we designed a multi-scale dilated causal convolutional module (DC-CNN) to capture local features, combined with a spatiotemporal dual-path Transformer to model long-range dependencies, and introduced relative position encoding to enhance temporal perception. The Sparrow Search Algorithm (SSA) was employed for global optimization of hyperparameters. Compared with five other mainstream algorithms, MSST-Net demonstrated higher accuracy in cable temperature prediction, achieving a coefficient of determination (R²), mean absolute error (MAE), and root mean square error (RMSE) of 0.942, 0.442°C, and 0.596°C, respectively. Compared to the basic Transformer model, the root mean square error of cable temperature was reduced by 0.425°C. This model exhibits high accuracy in time series prediction and provides a reference for accurate short- and medium-term temperature forecasting of cables in underground utility tunnels.