  In the field of algorithms and data structures analysis and design, most of
the researchers focus only on the space/time trade-off, and little attention
has been paid to energy consumption. Moreover, most of the efforts in the field
of Green Computing have been devoted to hardware-related issues, being green
software in its infancy. Optimizing the usage of computing resources,
minimizing power consumption or increasing battery life are some of the goals
of this field of research.
  As an attempt to address the most recent sustainability challenges, we must
incorporate the energy consumption as a first-class constraint when designing
new compact data structures. Thus, as a preliminary work to reach that goal, we
first need to understand the factors that impact on the energy consumption and
their relation with compression. In this work, we study the energy consumption
required by several integer vector representations. We execute typical
operations over datasets of different nature. We can see that, as commonly
believed, energy consumption is highly related to the time required by the
process, but not always. We analyze other parameters, such as number of
instructions, number of CPU cycles, memory loads, among others.
