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Artificial Intelligence and Machine Learning in Smart Grids

A special issue of Energies (ISSN 1996-1073). This special issue belongs to the section "A1: Smart Grids and Microgrids".

Deadline for manuscript submissions: 24 October 2024 | Viewed by 65

Special Issue Editors


E-Mail Website
Guest Editor
Faculty of Electronics and Information, Xi’an Jiaotong University, Xi’an 710049, China
Interests: smart grid; artificial intelligence

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Guest Editor
Electrical Engineering, Eindhoven University of Technology, 5600 MB Eindhoven, The Netherlands
Interests: DC power system; power electronics; power-to-heat

Special Issue Information

Dear Colleagues,

As societal demand for clean and efficient energy continues to escalate, smart grids are gradually emerging as a crucial means to achieve this objective. The application of emerging technologies such as artificial intelligence and machine learning in smart grids has become a pivotal driver for enhancing the efficiency and reliability of power systems, as well as catalysing the transition to sustainable energy sources. This Special Issue explores the application of artificial intelligence and machine learning in the field of smart grids, delving into the potential impact of these advanced technologies within the domain of power systems. The primary objective is to provide a comprehensive resource for researchers, practitioners, and decision-makers in the power sector, assisting them in better understanding and applying these technologies to propel the development of smart grids. Special emphasis is placed on their pivotal roles in data processing, predictive performance optimization, and fault detection, as well as the transformative effects they bring to the production, transmission, and distribution of electrical energy. We invite original and unpublished contributions for this Special Issue, focusing on innovative approaches to enhance artificial intelligence and machine learning technologies across all relevant applications in smart grids. The ultimate goal is to foster discussions and contributions that will advance the state of the art in these technologies, further driving innovation in the field of smart grids.

Additionally, please ensure that the summary aligns with the aims and scope of Energies: https://www.mdpi.com/journal/energies/about.

Dr. Donghe Li
Dr. Yu Xiao
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Energies is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • artificial intelligence
  • deep learning
  • machine learning
  • smart grid
  • artificial intelligence and machine learning techniques in smart grids
  • power quality measurement and assessment in smart grids using artificial intelligence and machine learning techniques

Published Papers

This special issue is now open for submission.
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