Machine Learning for Power Failure Prediction in Base Transceiver Stations: A Multivariate Approach
| dc.contributor.advisor | Dereje Hailemariam (PhD) | |
| dc.contributor.author | Sofia Ahmed | |
| dc.date.accessioned | 2023-12-14T14:48:23Z | |
| dc.date.available | 2023-12-14T14:48:23Z | |
| dc.date.issued | 2023-10 | |
| dc.description.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. | |
| dc.identifier.uri | http://etd.aau.edu.et/handle/123456789/951 | |
| dc.language.iso | en_US | |
| dc.publisher | Addis Ababa University | |
| dc.subject | BTS, Power failure, Deep learning, LSTM, CNN, hybrid CNN-LSTM Model | |
| dc.title | Machine Learning for Power Failure Prediction in Base Transceiver Stations: A Multivariate Approach | |
| dc.type | Thesis |