Machine Learning for Modern Power Distribution Systems: Progress and Perspectives

Marija Markovic, Matthew Bossart, Bri-Mathias Hodge

Research output: Contribution to journalArticlepeer-review


The application of machine learning (ML) to power and energy systems (PES) is being researched at an astounding rate, resulting in a significant number of recent additions to the literature. As the infrastructure of electric power systems evolves, so does interest in deploying ML techniques to PES. However, despite growing interest, the limited number of reported real-world applications suggests that the gap between research and practice is yet to be fully bridged. To help highlight areas where this gap could be narrowed, this article discusses the challenges and opportunities in developing and adapting ML techniques for modern electric power systems, with a particular focus on power distribution systems. These systems play a crucial role in transforming the electric power sector and accommodating emerging distributed technologies to mitigate the impacts of climate change and accelerate the transition to a sustainable energy future. The objective of this article is not to provide an exhaustive overview of the state-of-the-art in the literature, but rather to make the topic accessible to readers with an engineering or computer science background and an interest in the field of ML for PES, thereby encouraging cross-disciplinary research in this rapidly developing field. To this end, the article discusses the ways in which ML can contribute to addressing the evolving operational challenges facing power distribution systems and identifies relevant application areas that exemplify the potential for ML to make near-term contributions. At the same time, key considerations for the practical implementation of ML in power distribution systems are discussed, along with suggestions for several potential future directions.

Original languageAmerican English
Article numberArticle No. 032301
Number of pages26
JournalJournal of Renewable and Sustainable Energy
Issue number3
StatePublished - 1 May 2023

Bibliographical note

Publisher Copyright:
© 2023 Author(s).

NREL Publication Number

  • NREL/JA-6A40-87008


  • electric power systems
  • machine learning
  • power distribution systems


Dive into the research topics of 'Machine Learning for Modern Power Distribution Systems: Progress and Perspectives'. Together they form a unique fingerprint.

Cite this