A robust modeling framework for energy analysis of data centers

TL;DR

This paper introduces a hybrid modeling framework combining technology-based and data-driven methods to improve energy analysis of data centers, addressing current model limitations and aiding policy and decision-making.

Contribution

A novel hybrid modeling framework that enhances energy analysis of data centers by filling gaps in existing models with comprehensive, high-dimensional, and predictive capabilities.

Findings

Addresses data gaps in current models

Provides detailed analysis of data center energy use

Supports policy and investment decisions

Abstract

Global digitalization has given birth to the explosion of digital services in approximately every sector of contemporary life. Applications of artificial intelligence, blockchain technologies, and internet of things are promising to accelerate digitalization further. As a consequence, the number of data centers, which provide the services of data processing, storage, and communication services, is also increasing rapidly. Because data centers are energy-intensive with significant and growing electricity demand, an energy model of data centers with temporal, spatial, and predictive analysis capability is critical for guiding industry and governmental authorities for making technology investment decisions. However, current models fail to provide consistent and high dimensional energy analysis for data centers due to severe data gaps. This can be further attributed to the lack of the modeling capabilities for energy analysis of data center components including IT equipment and data center cooling and power provisioning infrastructure in current energy models. In this research, a technology-based modeling framework, in hybrid with a data-driven approach, is proposed to address the knowledge gaps in current data center energy models. The research aims to provide policy makers and data center energy analysts with comprehensive understanding of data center energy use and efficiency opportunities and a better understanding of macro-level data center energy demand and energy saving potentials, in addition to the technological barriers for adopting energy efficiency measures.