A Bidirectional Gated Recurrent Unit Model for PUE Prediction in Data Centers

TL;DR

This paper introduces a Bidirectional Gated Recurrent Unit (BiGRU) model for predicting Power Usage Effectiveness (PUE) in data centers, demonstrating improved accuracy over traditional GRU models through feature selection and hyperparameter optimization.

Contribution

The paper presents a novel BiGRU-based PUE prediction model with feature selection and hyperparameter tuning, outperforming existing models in data center energy efficiency prediction.

Findings

BiGRU model outperforms GRU in PUE prediction accuracy.

Feature selection improves model performance.

Optimized hyperparameters enhance prediction metrics.

Abstract

Data centers account for significant global energy consumption and a carbon footprint. The recent increasing demand for edge computing and AI advancements drives the growth of data center storage capacity. Energy efficiency is a cost-effective way to combat climate change, cut energy costs, improve business competitiveness, and promote IT and environmental sustainability. Thus, optimizing data center energy management is the most important factor in the sustainability of the world. Power Usage Effectiveness (PUE) is used to represent the operational efficiency of the data center. Predicting PUE using Neural Networks provides an understanding of the effect of each feature on energy consumption, thus enabling targeted modifications of those key features to improve energy efficiency. In this paper, we have developed Bidirectional Gated Recurrent Unit (BiGRU) based PUE prediction model and compared the model performance with GRU. The data set comprises 52,560 samples with 117 features using EnergyPlus, simulating a DC in Singapore. Sets of the most relevant features are selected using the Recursive Feature Elimination with Cross-Validation (RFECV) algorithm for different parameter settings. These feature sets are used to find the optimal hyperparameter configuration and train the BiGRU model. The performance of the optimized BiGRU-based PUE prediction model is then compared with that of GRU using mean squared error (MSE), mean absolute error (MAE), and R-squared metrics.