A HYBRID TCN-LSTM MODEL FOR PREDICTIVE MAINTENANCE OF AWS POWER SUPPLIES
TL;DR
The findings confirm that integrating these networks effectively balances rapid anomaly detection with stable long-term trend forecasting, and can be integrated into AWS telemetry systems as a robust diagnostic tool, providing automated early warnings to prevent critical power failures.
Abstract
Reliable power supply units are essential for Automatic Weather Stations (AWS) to maintain continuous data collection. However, traditional maintenance schedules often fail to prevent sudden equipment downtime. While machine learning can enable predictive maintenance, standard standalone models typically struggle to capture both immediate short-term anomalies and slow, long-term degradation. To address this gap, this study aims to evaluate and propose a hybrid Temporal Convolutional Network (TCN) and Long Short-Term Memory (LSTM) architecture specifically designed for AWS power supply forecasting. Using empirical time-series data, we monitored five operational parameters at 10-minute intervals from September 2023 to November 2024. Correlation analysis established battery temperature as a primary health indicator due to its strong inverse relationship with voltage (r = –0.87). Comparative evaluations demonstrated that while individual TCN and LSTM models exhibited architectural trade-offs, the proposed hybrid TCN-LSTM model achieved the highest predictive accuracy (R² = 0.9497; MAPE = 0.05%). The findings confirm that integrating these networks effectively balances rapid anomaly detection with stable long-term trend forecasting. Practically, this hybrid model can be integrated into AWS telemetry systems as a robust diagnostic tool, providing automated early warnings to prevent critical power failures.