Global increases in living standards,
diminishing natural resources and
environmental concerns
place energy amongst the most important
global issues today. On the consumer
side, there is an
increasing need for more efficient,
smart, uses of energy, be it in
large-scale computing systems
and data warehouses, in homes or in
office buildings. On the producer side,
there is a push toward
the use of sustainable, green, energy
sources, which often come in the form of
less reliable sources
such as wind energy. In addition, future
energy systems are often envisioned to
be "smart", consisting
of massive amounts of small generators,
such as solar panels, located at
consumers, effectively
turning consumers into potential
producers whenever they have a surplus
of energy. The management,
control and planning of, and efficient
use of energy in (future) energy systems
brings about many
important challenges.
Energy systems are not only real-world
systems, they are also one of the most
important foundations
of the modern world. Especially with the
upcoming required changes to make more
efficient use of
energy and to shift towards a global use
of sustainable, green energy sources,
there are many challenges
in mathematics and computer science.
Real-world challenges, such as those
arising in (future) energy
systems, are typically highly complex
because of the many aspects to be
considered that are often
disregarded in theoretical research such
as dynamic changes, uncertainty and
multiple objectives.
In many situations therefore,
problem-specific algorithms are
infeasible or impractical. Instead, flexible
and powerful approaches such as
evolutionary algorithms (EAs) can often
provide viable solutions.
Typical real-world challenges that are
addressed by EAs are of the optimization
type. This covers the use
of EAs to optimize issues ranging from
energy consumption (e.g. scheduling,
memory/storage
management, communication protocols,
smart sensors, etc.) to the planning and
design of energy
systems at many levels, ranging from
small printed circuit boards to entire
transmission networks.
The aim of this workshop is to bring
together researchers interested in
addressing challenging issues
related to the use of evolutionary
computation for applications in (future)
energy systems. The workshop
is a follow up of the GreenIT
Evolutionary Computation workshop held
at GECCO 2011.
The workshop covers all energy-related
applications of evolutionary
computation, including but not limited to:
- planning of (future) (smart) energy
systems
- network design optimization
- management and control of (future)
(smart) energy systems
- stability of smart energy systems
- dynamic demand and supply matching in
smart energy systems
- smart homes, buildings, offices,
streets, ...
- energy-efficient optimization and its
applications
- energy-efficient scheduling algorithms
- optimization of energy-efficient protocols
- modeling-representations, simulation
and validation for energy consumption
optimization problems
- large scale and high-dimensional
energy-efficient optimization
- energy-aware smart grids
- thermal optimization in cloud
computing/data centers
- online dynamic optimization for energy
efficient systems
- energy optimization in uncertain
environments
- learning and anticipation
- robustness and performance guarantees
- real-world energy efficient
optimization problems
- management and profiling tools for
energy efficient systems
