Comparative Analysis of Machine Learning Models for Prediction of BTS Power System Failure

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Date

2025-06

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Addis Ababa University

Abstract

Loss of power system integrity in Base Transceiver Stations (BTS) can have significant impacts on communication networks, causing service outages and loss of revenue. This study was focused on developing and testing machine learning models to predict BTS power system failures so that maintenance can be performed before system failures occur. The models tested were Hidden Markov Models (HMM), Long Short-Term Memory (LSTM), and Random Forest (RF). The data used for this research included 46,943 records from ethiotelecom’s (ET) monitoring system. ET’s provided information related to variables sourced from monitoring features that impact power system failure, including environmental, load, battery, or other BTS-based metric records. All these data were pre-processed. Before feature analysis, Z-score normalization was conducted to standardize the data. After this data preparation step, principal component analysis (PCA) was undertaken to perform feature analysis. In addition, K-means clustering was also applied to categorize the hidden states (’Normal,’ ’Degraded,’ and ’Failure’) and group the observable sequences. The HMM was trained using the Baum-Welch algorithm, and the Viterbi technique aided state prediction. To enhance performance, a range of hyperparameter approaches were applied to the RF and LSTM models. With a 97.72% F1-score, 98.04% accuracy, 98.08% precision, and 98.04% recall, compared to the other two models, the HMM performed better when load-related parameters were observable. With an accuracy of 97.81% and an F1-score of 96.74%, the LSTM model came in second place for identifying temporal connections in the data. Despite being robust, RF’s performance metrics were marginally worse, with an F1-score of 93% and an accuracy of 95%. The study’s conclusions show that the best model for forecasting BTS power system breakdowns is HMM. Due to its exceptional precision and dependability, ethiotelecom can enhance network performance by facilitating proactive maintenance, reducing downtime, and increasing user satisfaction.

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Keywords

BTS, power system failure, HMM, LSTM, RF, prediction

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