  Implementing resource efficient energy management systems in facilities and
buildings becomes increasingly important in the transformation to a sustainable
society. However, selecting a suitable configuration based on multiple,
typically conflicting objectives, such as cost, robustness with respect to
uncertainty of grid operation, or renewable energy utilization, is a difficult
multi-criteria decision making problem. The recently developed concept
identification technique can facilitate a decision maker by sorting
configuration options into semantically meaningful groups (concepts). In this
process, the partitioning of the objectives and design parameters into
different sets (called description spaces) is a very important step. In this
study we focus on utilizing the concept identification technique for finding
relevant and viable energy management configurations from a very large data set
of Pareto-optimal solutions. The data set consists of 20000 realistic
Pareto-optimal building energy management configurations generated by a
many-objective evolutionary optimization of a high quality Digital Twin energy
management simulator. We analyze how the choice of description spaces, i.e.,
the partitioning of the objectives and parameters, impacts the type of
information that can be extracted. We show that the decision maker can
introduce constraints and biases into that process to meet expectations and
preferences. The iterative approach presented in this work allows for the
generation of valuable insights into trade-offs between specific objectives,
and constitutes a powerful and flexible tool to support the decision making
process when designing large and complex energy management systems.
