An Edge-Enabled Lightweight LSTM for the Temperature Prediction of Electrical Joints in Low-Voltage Distribution Cabinets

Abstract

Joint overheating in low-voltage distribution cabinets presents a major safety risk, often leading to insulation failure, accelerated aging, and even fires. Conventional threshold-based inspection methods are limited in detecting early temperature evolution and lack predictive capabilities. To address this, a short-term temperature prediction method for electrical joints based on deep learning is proposed. Using a self-developed sensing device and Raspberry Pi edge nodes, multi-source data—including voltage, current, power, and temperature—were collected and preprocessed. Comparative experiments with ARIMA, GRU, and LSTM models demonstrate that the LSTM achieves the highest prediction accuracy, with an RMSE, MAE, and MAPE of 0.26 °C, 0.21 °C, and 0.54%, respectively. Furthermore, a lightweight version of the model was optimized for edge deployment, achieving a comparable accuracy (RMSE = 0.27 °C, MAE = 0.21 °C, MAPE = 0.67%) while reducing the inference latency and memory cost. The model effectively captures temperature fluctuations during 6 h prediction tasks and maintains stability under different cabinet scenarios. These results confirm that the proposed edge-enabled lightweight LSTM model achieves a balanced trade-off between accuracy, real-time performance, and efficiency, providing a feasible technical solution for intelligent temperature monitoring and predictive maintenance in low-voltage distribution systems.