Abstract
In the field of energy system modelling, increasing complexity and optimization analysis are essential for understanding the most effective decarbonization options. However, the growing need for intricate models leads to increased computational time, which can hinder progress in research and policy-making. This study aims to address this issue by integrating machine learning algorithms with EnergyPLAN and EPLANopt, a coupling of EnergyPLAN software and a multi-objective evolutionary algorithm, to expedite the optimization process while maintaining accuracy. By saving computational time, we can increase the number of simulations, thereby enabling deeper exploration of uncertainty in energy system modelling. This allows researchers to better understand the trade-offs and best practices for decarbonization,
which could lead to more informed policy decisions and optimized energy systems. This article introduces a novel approach to energy system modelling optimization by integrating machine learning algorithms with EnergyPLAN and EPLANopt. The resulting methodology can accelerate the optimization process and provide deeper insights into uncertainty, ultimately contributing to more effective decarbonization strategies.