  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.
