Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
6.2
3.0
Journals
Energies
Special Issues
Artificial Intelligence and Machine Learning Applications in Smart Energy Systems
energies-logo
Submit to Special Issue Submit Abstract to Special Issue Review for Energies Propose a Special Issue

Journal Menu

► Journal Menu

Journal Browser

► Journal Browser
share announcement

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 2208

Special Issue Editors

Dr. Marcin Blachnik

E-Mail Website
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
Prof. Dr. Grzegorz Dudek

E-Mail Website
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
Prof. 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

Benefits of Publishing in a Special Issue

  • Ease of navigation: Grouping papers by topic helps scholars navigate broad scope journals more efficiently.
  • Greater discoverability: Special Issues support the reach and impact of scientific research. Articles in Special Issues are more discoverable and cited more frequently.
  • Expansion of research network: Special Issues facilitate connections among authors, fostering scientific collaborations.
  • External promotion: Articles in Special Issues are often promoted through the journal's social media, increasing their visibility.
  • e-Book format: Special Issues with more than 10 articles can be published as dedicated e-books, ensuring wide and rapid dissemination.

Further information on MDPI's Special Issue polices can be found here.

Published Papers (2 papers)

Download All Papers
Order results
Result details
Show export options expand_more Show export options expand_less
Select all
Export citation of selected articles as:

Research

18 pages, 4375 KiB  
Article
Research on Oil Well Production Prediction Based on GRU-KAN Model Optimized by PSO
by Bo Qiu, Jian Zhang, Yun Yang, Guangyuan Qin, Zhongyi Zhou and Cunrui Ying
Energies 2024, 17(21), 5502; https://doi.org/10.3390/en17215502 - 4 Nov 2024
Viewed by 680
Abstract
Accurately predicting oil well production volume is of great significance in oilfield production. To overcome the shortcomings in the current study of oil well production prediction, we propose a hybrid model (GRU-KAN) with the gated recurrent unit (GRU) and Kolmogorov–Arnold network (KAN). The [...] Read more.
Accurately predicting oil well production volume is of great significance in oilfield production. To overcome the shortcomings in the current study of oil well production prediction, we propose a hybrid model (GRU-KAN) with the gated recurrent unit (GRU) and Kolmogorov–Arnold network (KAN). The GRU-KAN model utilizes GRU to extract temporal features and KAN to capture complex nonlinear relationships. First, the MissForest algorithm is employed to handle anomalous data, improving data quality. The Pearson correlation coefficient is used to select the most significant features. These selected features are used as input to the GRU-KAN model to establish the oil well production prediction model. Then, the Particle Swarm Optimization (PSO) algorithm is used to enhance the predictive performance. Finally, the model is evaluated on the test set. The validity of the model was verified on two oil wells and the results on well F14 show that the proposed GRU-KAN model achieves a Root Mean Square Error (RMSE), Mean Absolute Error (MAE), Mean Absolute Percentage Error (MAPE) and Coefficient of Determination (R2) values of 11.90, 9.18, 6.0% and 0.95, respectively. Compared to popular single and hybrid models, the GRU-KAN model achieves higher production-prediction accuracy and higher computational efficiency. The model can be applied to the formulation of oilfield-development plans, which is of great theoretical and practical significance to the advancement of oilfield technology levels. Full article
(This article belongs to the Special Issue Artificial Intelligence and Machine Learning Applications in Smart Energy Systems)
►▼ Show Figures

Figure 1

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 795
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
(This article belongs to the Special Issue Artificial Intelligence and Machine Learning Applications in Smart Energy Systems)
►▼ Show Figures

Figure 1

Show export options expand_more Show export options expand_less
Select all
Export citation of selected articles as:
 
Displaying articles 1-2
Back to TopTop