Machine Learning for Improved Root Cause Analysis of Data Center Energy Inefficiency
| dc.contributor.advisor | Dereje Hailemariam (PhD) | |
| dc.contributor.author | Elsa Abreha | |
| dc.date.accessioned | 2025-08-01T11:55:21Z | |
| dc.date.available | 2025-08-01T11:55:21Z | |
| dc.date.issued | 2025-06 | |
| dc.description.abstract | In this study the vital issue of energy inefficiency in the data center is addressed by developing a machine learning-based framework for root cause analysis (RCA) of high power use efficiency (PUE) rates. Focusing on the Nefas Silk Data Center, the research leverages a 1D Convolutional Neural Network (CNN) model to classify PUE efficiency and employs SHapley Additive exPlanations (SHAP) to interpret the contributions of key operational features. The dataset, comprising 6,586 hourly measurements, identifies air conditioning systems as the primary driver of inefficiency, followed by UPS losses during power conversion and distribution, and rectifier performances. The suggested 1D CNN model demonstrates outstanding performance, achieving an accuracy of 99.99%, sensitivity of 99.99%, and an F1 score of 99.99%, outperforming the comparative LSTM and RNN architectures. By integrating global and local interpretability methods, the framework provides recommendations to optimize energy consumption, reduce operational costs, and improve sustainability. The findings underscore the potential of machine learning to transform data center energy management, offering a scalable solution to improve efficiency in similar infrastructures. Future work will aim to improve energy efficiency in data centers by improving root cause analysis through the integration of historical data from all data centers at the core site. It will also involve comparative studies to assess regional factors that influence performance. These initiatives seek to create a robust framework for sustainable energy management in various environments. | |
| dc.identifier.uri | https://etd.aau.edu.et/handle/123456789/5889 | |
| dc.language.iso | en_US | |
| dc.publisher | Addis Ababa University | |
| dc.subject | Energy efficiency in the data center | |
| dc.subject | Power Usage Effectiveness (PUE) | |
| dc.subject | Root Cause Analysis (RCA) | |
| dc.subject | 1D CNN | |
| dc.subject | SHAP values | |
| dc.subject | Machine Learning Interpretability. | |
| dc.title | Machine Learning for Improved Root Cause Analysis of Data Center Energy Inefficiency | |
| dc.type | Thesis |