Prediction of Base Transceiver Station Power Supply System Failure Indicators using Deep Neural Networks for Multi-Time Variant Time Series
| dc.contributor.advisor | Dereje Hailemariam (PhD) | |
| dc.contributor.author | Jalene Bekuma | |
| dc.date.accessioned | 2024-03-12T15:24:07Z | |
| dc.date.available | 2024-03-12T15:24:07Z | |
| dc.date.issued | 2023-11 | |
| dc.description.abstract | The uninterrupted operation of wireless communication services relies heavily on the stability of power supply systems for Base Transceiver Stations (BTS). This study is dedicated to predicting potential failure indicators in BTS power systems using deep neural network architectures, such as recurrent and convolutional neural networks. The study integrates principal component analysis (PCA) for data dimensionality reduction and addresses challenges related to power system failures caused by environmental factors, power fluctuations, and equipment malfunctions within the Ethio telecom BTS system. The dataset utilized in this study spans four weeks of data from multiple sites, with observations sampled at 5-minute intervals, obtained from the ET NetEcho power monitoring system. The study meticulously explores the data preprocessing steps for time series analysis, encompassing consolidation, cleaning, scaling, and dimensionality reduction using PCA. Furthermore, it delves into the detailed implementation of CNN, LSTM, and CNN-LSTM models for time series prediction, thoroughly evaluating their performance and convergence. The experimental results clearly indicate that CNN-LSTM model surpasses both LSTM and CNN models in predicting BTS power system failure indicators, achieving the lowest loss values of 0.036 MSE, 0.189 RMSE and 0.112 MAE using CNN_LSTM model. These findings shows the potential of deep neural network architectures, particularly CNN_LSTM model in accurately predicting BTS power system failure indicators for the next thirty minutes. The significance of accurate prediction models in proactively detecting failures and minimizing their impact is highlighted, contributing to the reliability and stability of BTS power supply systems for wireless communication services. | |
| dc.identifier.uri | https://etd.aau.edu.et/handle/123456789/2395 | |
| dc.language.iso | en_US | |
| dc.publisher | Addis Ababa University | |
| dc.subject | BTS, Deep Neural Network, Power System, System Failure | |
| dc.title | Prediction of Base Transceiver Station Power Supply System Failure Indicators using Deep Neural Networks for Multi-Time Variant Time Series | |
| dc.type | Thesis |