  Energy systems optimization problems are complex due to strongly non-linear
system behavior and multiple competing objectives, e.g. economic gain vs.
environmental impact. Moreover, a large number of input variables and different
variable types, e.g. continuous and categorical, are challenges commonly
present in real-world applications. In some cases, proposed optimal solutions
need to obey explicit input constraints related to physical properties or
safety-critical operating conditions. This paper proposes a novel data-driven
strategy using tree ensembles for constrained multi-objective optimization of
black-box problems with heterogeneous variable spaces for which underlying
system dynamics are either too complex to model or unknown. In an extensive
case study comprised of synthetic benchmarks and relevant energy applications
we demonstrate the competitive performance and sampling efficiency of the
proposed algorithm compared to other state-of-the-art tools, making it a useful
all-in-one solution for real-world applications with limited evaluation
budgets.
