Comparative Analysis of Machine Learning Models for Prediction of BTS Power System Failure
No Thumbnail Available
Date
2025-06
Authors
Journal Title
Journal ISSN
Volume Title
Publisher
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.
Description
Keywords
BTS, power system failure, HMM, LSTM, RF, prediction