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Machine Learning for Next-Generation Power Systems: Challenges and Opportunities
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Machine Learning for Next-Generation Power Systems: Challenges and Opportunities

A special issue of Electronics (ISSN 2079-9292). This special issue belongs to the section "Power Electronics".

Deadline for manuscript submissions: closed (15 February 2026) | Viewed by 1775

Special Issue Editor

Dr. Nastaran Gholizadeh

E-Mail Website
Guest Editor
Electrical and Computer Engineering, University of Alberta, Edmonton, AB T6G 1H9, Canada
Interests: machine learning; distributed learning; federated learning; reinforcement learning; power systems; privacy

Special Issue Information

Dear Colleagues,

The integration of machine learning into the field of power systems represents a transformative shift to revolutionize the way we generate, distribute, and consume electricity. As the global energy landscape evolves, driven by increasing demand, the proliferation of renewable energy sources, and the urgency of mitigating climate change, the need for advanced, intelligent solutions in power systems has never been more critical. This Special Issue, "Machine Learning for Next-Generation Power Systems: Challenges and Opportunities", aims to explore the forefront of this burgeoning field, presenting cutting-edge research, innovations, and applications that demonstrate the potential and address the complexities of leveraging machine learning in modern power systems.

This Special Issue aims to collect high-quality, original research articles and in-depth reviews that explore both theoretical and practical aspects of applying machine learning to power systems. By encouraging interdisciplinary research and collaboration, we hope to pave the way for innovative solutions that enhance the efficiency, reliability, and sustainability of global power systems. Research areas may include (but are not limited to) the following:

Machine Learning Algorithms for:

  • Power System Operation Optimization;
  • Power System Planning;
  • Predictive Maintenance and Fault Detection;
  • Renewable Energy Integration;
  • Smart Grid Technologies;
  • Energy Consumption Forecasting;
  • Real-Time Energy Management;
  • Enhancing Grid Stability and Reliability;
  • Data-Driven Decision Making in Power Systems;
  • Cybersecurity in Power Systems;
  • Energy Storage Systems Optimization;
  • Demand Response and Consumer Behavior;
  • Policy and Regulatory Implications.

I look forward to receiving your contributions.

Dr. Nastaran Gholizadeh
Guest Editor

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 250 words) can be sent to the Editorial Office for assessment.

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. Electronics 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 2400 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

  • machine learning
  • big data
  • power systems
  • predictive maintenance
  • fault detection
  • energy forecasting
  • real-time management
  • data-driven decision making
  • cybersecurity

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Published Papers (2 papers)

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Research

26 pages, 10348 KB  
Article
A Resilient Ensemble Deep Learning Architecture for Load Forecasting Against FDI Attack
by Zhenya Chen, Yameng Zhang, Bin Liu, Ming Yang and Xuguo Jiao
Electronics 2026, 15(5), 991; https://doi.org/10.3390/electronics15050991 - 27 Feb 2026
Viewed by 383
Abstract
Short-term load forecasting (STLF) is crucial for ensuring power grid stability and economic dispatch. Its accuracy heavily depends on the quality of the input data. However, collecting operational data via the power system’s communication network poses a significant vulnerability to cyberattacks, particularly stealthy [...] Read more.
Short-term load forecasting (STLF) is crucial for ensuring power grid stability and economic dispatch. Its accuracy heavily depends on the quality of the input data. However, collecting operational data via the power system’s communication network poses a significant vulnerability to cyberattacks, particularly stealthy False Data Injection (FDI) attacks. By closely mimicking normal load fluctuations, these attacks evade conventional detection, thus, compromising forecasting reliability. To address this challenge, this paper proposes a novel resilient load forecasting framework that integrates two-stage attack detection with robust ensemble learning. In the detection stage, attack identification is performed through seasonal decomposition and AE-BiLSTM reconstruction, followed by restoration using periodic-consistent historical means and secondary screening via second-order differencing (SOD). In the forecasting stage, an improved Multi-Objective Whale Migration Algorithm (MO-WMA) is employed to adaptively optimize ensemble weights for intelligent fusion, significantly enhancing prediction accuracy and robustness, and providing a generalizable solution for intelligent grid load forecasting. Experiments were conducted on the Independent System Operator of New England (ISO New England, 2012–2014) load dataset under four typical FDI attack scenarios, with test sets including diverse attack intensities and temporal patterns. Results show that the framework achieves 98.98% attack detection accuracy and improves the R2 forecasting metric from 0.9053 to 0.9851, approaching attack-free performance, demonstrating effective recovery of forecasting accuracy and generalization capability. Full article
(This article belongs to the Special Issue Machine Learning for Next-Generation Power Systems: Challenges and Opportunities)
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15 pages, 1493 KB  
Article
Energy-Efficient User Association with Multi-Objective Optimization for Full-Duplex C-RAN Enabled Massive MIMO Systems
by Shruti Sharma and Wonsik Yoon
Electronics 2025, 14(21), 4197; https://doi.org/10.3390/electronics14214197 - 27 Oct 2025
Viewed by 672
Abstract
In this study, we developed an energy-efficient multi-user-associated optimization method involving a massive multi-input multi-output (M-MIMO) system-enabled Cloud Radio Access Network (C-RAN) in Full-Duplex (FD) mode. Maximization of energy efficiency (EE) was achieved with user association. We compose the non-convex multi-objective optimization (MOO) [...] Read more.
In this study, we developed an energy-efficient multi-user-associated optimization method involving a massive multi-input multi-output (M-MIMO) system-enabled Cloud Radio Access Network (C-RAN) in Full-Duplex (FD) mode. Maximization of energy efficiency (EE) was achieved with user association. We compose the non-convex multi-objective optimization (MOO) problem for resource allocation and user association in C-RAN. The resultant non-convex MOO problem is non-deterministic polynomial (NP) hard. To tackle this complexity, we find a trade-off between achievable rate and energy consumption. We first reaffirm the problem as an MOO targeting high throughput and minimizing energy consumption instantaneously. By using the epsilon (ε)-constraint method, we transform MOO to an equivalent single objective optimization (SOO) problem by majorization–minimization (MM) approach that enables the transformation of binaries into continuous variables. Further, we propose a multi-objective resource allocation algorithm to obtain a Pareto optimal solution. The simulation results show a significant gain in EE of C-RAN achieved through our proposed MOO algorithm. Our results also show remarkable trade-offs between EE and spectral efficiency (SE). Full article
(This article belongs to the Special Issue Machine Learning for Next-Generation Power Systems: Challenges and Opportunities)
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