The increasing transformation of the European energy mar-
ket, driven by the rise of intermittent renewable energies, the decommis-
sioning of controllable power plants and dependence on short-term stor-
age, poses challenges for assessing security of supply. In order to evaluate
the capacity of available generation to meet demand in uncertain condi-
tions, market model optimizations using the Monte Carlo (MC) approach
are employed. However, the high computational costs of this approach
limit assessment resolution. This paper investigates metamodeling as a
strategy to reduce these computational costs. Metamodelling is a process
of using mathematical models on a subset of simulations to map outcomes
to the input data. Thisreduces the total number of simulations required.
The study explores three key steps: enhancing input-output correlation,
identifying effective machine learning (ML) models and selecting optimal
training samples. While no single model performs adequately due to data
complexity, a two-model pipeline significantly improves prediction accu-
racy. An active learning approach is also introduced to further optimize
sample selection. The results show that training on only twenty per-
cent of the data reduces the computation time by more than 75 percent,
with a relative error below 10 percent. These findings demonstrate the
potential of metamodeling to enable efficient, high-resolution resource
adequacy assessments in evolving energy systems.