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Artificial Intelligence and Machine Learning Applications in Smart Energy Systems

A special issue of Energies (ISSN 1996-1073). This special issue belongs to the section "F5: Artificial Intelligence and Smart Energy".

Deadline for manuscript submissions: 31 December 2024 | Viewed by 723

Special Issue Editors


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Guest Editor
Department of Industrial Informatics, Faculty of Materials Engineering, Silesian University of Technology, Akademicka 2A, 44-100 Gliwice, Poland
Interests: data mining; artificial intelligence; machine learning; energy systems
Special Issues, Collections and Topics in MDPI journals

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Guest Editor
Department of Automatic Control, Electrical Engineering and Optoelectronics, Faculty of Electrical Engineering, Częstochowa University of Technology, Al. Armii Krajowej 17, 42-200 Częstochowa, Poland
Interests: machine learning; evolutionary computation; artificial intelligence; pattern recognition; data mining and applications in forecasting, classification, regression, and optimization problems
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

We are delighted to announce a Special Issue on "Artificial Intelligence and Machine Learning Applications in Smart Energy Systems." The main goal of this Special Issue is to bring together the latest research and developments in the areas of artificial intelligence (AI) and machine learning (ML) for smart energy systems.

As the demand for energy continues to increase, smart energy systems are becoming more prevalent in addressing the challenges associated with energy generation, distribution, and consumption. AI and ML have been identified as promising approaches to address these challenges, as they improve the efficiency, reliability, and sustainability of smart energy systems. Thus, this Special Issue aims to present original research articles, review papers, and case studies that demonstrate innovative applications of AI and ML in smart energy systems.

Topics of interest include, but are not limited to:

  • Machine learning for energy forecasting;
  • Artificial intelligence in demand response;
  • Intelligent control and optimization of energy systems;
  • Big data analytics for smart grids;
  • Reinforcement learning for energy management;
  • Deep learning for energy system modeling and simulation;
  • Cybersecurity and privacy in smart energy systems;
  • Human–machine interactions and decision making in smart energy systems.

We invite researchers and practitioners to submit their original research and review papers on these and other related topics. All submitted manuscripts will undergo a rigorous peer-review process to ensure they are high quality and original. We look forward to your valuable contributions to this Special Issue.  

Dr. Marcin Blachnik
Dr. Grzegorz Dudek
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

  • smart energy systems
  • modeling
  • energy market
  • smart homes
  • renewable energy sources
  • smart modeling
  • machine learning
  • optimization
  • artificial intelligence
  • forecasting
  • load management
  • renewable energy integration
  • energy efficiency optimization
  • demand response
  • power system stability
  • fault detection and diagnosis
  • cybersecurity in energy systems
  • big data analytics
  • Internet of Things (IoT)
  • distributed energy resources
  • energy storage systems

Published Papers (1 paper)

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Research

21 pages, 985 KiB  
Article
Integrating an Ensemble Reward System into an Off-Policy Reinforcement Learning Algorithm for the Economic Dispatch of Small Modular Reactor-Based Energy Systems
by Athanasios Ioannis Arvanitidis and Miltiadis Alamaniotis
Energies 2024, 17(9), 2056; https://doi.org/10.3390/en17092056 - 26 Apr 2024
Viewed by 335
Abstract
Nuclear Integrated Energy Systems (NIES) have emerged as a comprehensive solution for navigating the changing energy landscape. They combine nuclear power plants with renewable energy sources, storage systems, and smart grid technologies to optimize energy production, distribution, and consumption across sectors, improving efficiency, [...] Read more.
Nuclear Integrated Energy Systems (NIES) have emerged as a comprehensive solution for navigating the changing energy landscape. They combine nuclear power plants with renewable energy sources, storage systems, and smart grid technologies to optimize energy production, distribution, and consumption across sectors, improving efficiency, reliability, and sustainability while addressing challenges associated with variability. The integration of Small Modular Reactors (SMRs) in NIES offers significant benefits over traditional nuclear facilities, although transferring involves overcoming legal and operational barriers, particularly in economic dispatch. This study proposes a novel off-policy Reinforcement Learning (RL) approach with an ensemble reward system to optimize economic dispatch for nuclear-powered generation companies equipped with an SMR, demonstrating superior accuracy and efficiency when compared to conventional methods and emphasizing RL’s potential to improve NIES profitability and sustainability. Finally, the research attempts to demonstrate the viability of implementing the proposed integrated RL approach in spot energy markets to maximize profits for nuclear-driven generation companies, establishing NIES’ profitability over competitors that rely on fossil fuel-based generation units to meet baseload requirements. Full article
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