Machine Learning for Energy Systems Optimization.

While ML algorithms have improved conventional ES optimization models, new models using ML techniques have become increasingly important [[55]]. These ML algorithms that have focused on the optimization of ESs aim to accelerate the conventional ES optimization models, decentralize the centralized optimization models, or speed them up by approximating the iteration processes and parameters related to optimization. This editorial overviews the contents of the Special Issue "Machine Learning for Energy Systems 2021" and review the trends in machine learning (ML) techniques for energy system (ES) optimization. In other words, a few ML algorithms have improved the optimization of current ES optimization models rather than been used in the development of new models. [Extracted from the article]