Machine learning for base transceiver stations power failure prediction: A multivariate approach

• Published in: e-Prime 2024-12, Vol.10, p.100814, Article 100814
• Main Authors: Ahmed, Sofia, Terefe, Tsegamlak, Hailemariam, Dereje
• Format: Article
• Language: English
• Subjects: BTS; CNN; Deep learning; Hybrid CNN-LSTM model; LSTM; Power failure
• ISSN: 2772-6711; 2772-6711
• DOI: 10.1016/j.prime.2024.100814

The widespread deployment of cellular networks has improved communication access, driving economic growth and enhancing social connections across diverse regions. Base Transceiver Stations (BTSs), are foundational to mobile networks but are vulnerable to power failures, disrupting service delivery and causing user inconvenience. This paper proposes a machine-learning-based framework for preemptive BTS power failure prediction using multivariate time-series data from power and environmental monitoring systems. We employ a combination of deep learning architectures, including Convolutional Neural Networks (CNNs), Long Short-Term Memory (LSTM) networks, and hybrid CNN-LSTM models, to achieve accurate and timely predictions of BTS power failures. CNNs were selected for extracting dependencies among features of a multivariate time-series data, while LSTMs effectively capture temporal dependencies, making them suitable for predicting power failures. The proposed models exhibit noteworthy predictive performance, with the LSTM network emerging as the most accurate model (MSE: 0.001, MAPE: 2.528), followed by the hybrid CNN-LSTM (MSE: 0.001, MAPE: 2.843) and the CNN (MSE: 0.223, MAPE: 2.843). This work demonstrates deep learning’s effectiveness in preemptive BTS failure prediction, enabling proactive maintenance and improved network resilience. •Outline the consequences of power failure at Base Transceiver Stations (BTS).•Propose predictive models for power failure using deep neural networks.•Identify and analyze features that are useful in power failure prediction.•Interpret and justify predicted outcomes in the context of quality of service and financial gains.