Machine Learning for Improved Root Cause Analysis of Data Center Energy Inefficiency
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Date
2025-06
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Addis Ababa University
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.
Description
Keywords
Energy efficiency in the data center, Power Usage Effectiveness (PUE), Root Cause Analysis (RCA), 1D CNN, SHAP values, Machine Learning Interpretability.