Tool-assisted Surrogate Selection for Simulation Models in Energy Systems

Abstract

Surrogate models have proved to be a suitable replacement for complex simulation models in various applications. Runtime considerations, complexity reduction, and privacy concerns play a role in the decision to use a surrogate model. The choice of an appropriate surrogate model though is often tedious and largely dependent on the individual model properties. A tool can help to facilitate this process. To this end, we present a surrogate modeling process supporting tool that simplifies the process of generation and application of surrogate models in a co-simulation framework. We evaluate the tool in our application context, energy system co-simulation, and apply it to different simulation models from that domain with a focus on decentralized energy units.

Evaluating different machine learning techniques as surrogate for low voltage grids

“…Surrogate models can be found in many domains, but we will focus on the energy domain. They are used in a broad range of use cases: starting from the calculation and optimization of energy savings [8,9,10] and the replacement of specific simulation models [11,12] over surrogate models for (micro)grids [13,14,15] to the use in uncertainty and reliability assessment [16,17,2]. This list is far from complete and there are also other approaches such as in Gerster [18] who use surrogate models to build a decoder function abstracting from technical system specifications.…”

A Two-Stage Monte Carlo Approach for Optimization of Bimetallic Nanostructures

“…It is important that such approach as the formation energy machine learning model [9] can be used to predict the stable metal element distribution in the nanoparticles via Monte Carlo simulations. In [10], Monte Carlo sampling for pure random selection of sample points is used. It can be useful when implementing the so-called surrogate models, which can be a suitable replacement for complex simulation models in applications.…”