Machine Learning for Power Failure Prediction in Base Transceiver Stations: A Multivariate Approach
No Thumbnail Available
Date
2023-10
Authors
Journal Title
Journal ISSN
Volume Title
Publisher
Addis Ababa University
Abstract
The proliferation of mobile cellular networks has had a transformative impact on economic
and social activities. Base transceiver stations (BTSs) play a critical role in delivering wireless
services to mobile users. However, power failures in BTSs can pose significant challenges to
maintaining uninterrupted mobile services, leading to inconveniences for users and financial
losses for service providers.
This thesis introduces a novel approach to mitigating power system interruptions in BTSs
using a machine learning-based power failure prediction framework. The framework leverages
multivariate time-series data collected from the BTS power and environmental monitoring
system. The methodology aims to preemptively predict power failures using three advanced
machine learning techniques, specifically, Convolutional Neural Networks (CNNs), Long Shortterm
Memory (LSTM), and CNN-LSTM networks. These methods excel in capturing complex
temporal relationships inherent in time-series data.
All the three algorithms reasonably capture the temporal patterns in the data. However,
the LSTM model consistently outperforms the other two models having a MSE of 0.001 and
1.194 MAPE, albeit with longer training times which is more than three hours. On the other
hand, the CNN-LSTM model stands out for its efficient training process, which takes notably
less time than the LSTM model around two hours training time resulting 0.001 MSE and 2.528
MAPE. Furthermore, the CNN model takes notably less time to compute than the other two
models with a prediction performance of 0.223 MSE and 2.843 MAPE.
essential to highlight that this study concentrates on the predictive aspect, which contributes
significantly to the field by offering a robust and effective predictive model tailored
specifically for BTS power systems. By enabling timely maintenance actions and minimizing
downtime, our proposed methodology holds the possibility to significantly improve the reliability
of telecommunications infrastructure, which will ultimately lead to better user experiences
and streamlined service provider operations.
Description
Keywords
BTS, Power failure, Deep learning, LSTM, CNN, hybrid CNN-LSTM Model