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Artificial Intelligence-Based Approaches for Power Energy System Modelling
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Artificial Intelligence-Based Approaches for Power Energy System Modelling

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: 10 December 2026 | Viewed by 966

Special Issue Editors

Dr. Jose Luis Calvo-Rolle

E-Mail Website
Guest Editor
Department of Industrial Engineering, University of A Coruña, CTC, CITIC, Ferrol, 15071 A Coruña, Spain
Interests: knowledge engineering and expert systems for diagnosis and control; intelligent systems for modelling, optimization, and control; fault and anomaly detection using traditional and intelligent techniques; new sensors; robust sensors; virtual sensors
Special Issues, Collections and Topics in MDPI journals
Dr. Francisco Zayas-Gato

E-Mail Website
Guest Editor
Department of Industrial Engineering, University of A Coruña, CTC, CITIC, Ferrol, 15071 A Coruña, Spain
Interests: modelling, optimization, and control based on intelligent systems; fault and anomaly detection based on traditional and intelligent techniques; artificial intelligence; power energy systems
Special Issues, Collections and Topics in MDPI journals
Dr. Esteban Pérez

E-Mail Website
Guest Editor
Department of Industrial Engineering, University of A Coruña, CTC, CITIC, Ferrol, 15071 A Coruña, Spain
Interests: knowledge engineering and expert systems for diagnosis and control; intelligent systems for modelling, optimization, and control; anomaly detection using traditional and intelligent techniques; new sensors; robust sensors and virtual sensors; artificial intelligence; power energy systems
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

In recent years, the integration of Artificial Intelligence (AI) techniques into power and energy systems has revolutionized how these are modelled, optimized, and managed. The increasing complexity of modern energy infrastructures—driven by the rise of renewable energy sources, smart grids, electric vehicles, and distributed generation—demands intelligent solutions capable of handling uncertainty, nonlinearity, and real-time decision-making. This Special Issue explores the latest AI-based methodologies and their applications in the modelling and analysis of power energy systems.

AI approaches such as machine, deep, and reinforcement learning, swarm intelligence, and evolutionary algorithms have demonstrated significant potential in enhancing the reliability, efficiency, and sustainability of power systems. These methods enable improved load forecasting, fault diagnosis, optimal power flow, energy demand prediction, and real-time control. By learning from data and adapting to dynamic environments, AI techniques provide robust tools for modelling systems that are too complex for analysis using traditional methods.

This Special Issue aims to gather cutting-edge research that addresses key challenges and proposes novel AI-driven solutions for the power energy sector. Contributions may include theoretical advancements, innovative applications, case studies, or hybrid methodologies that combine AI with classical modelling techniques. The topics of interest include, but are not limited to, AI-based grid management, predictive maintenance, energy storage optimization, smart metering analysis, and the integration of distributed energy resources.

We invite researchers, practitioners, and industry experts to contribute high-quality papers that will not only advance our academic understanding but also foster the practical deployment of AI solutions in modern power systems. Through this Special Issue, we seek to bridge the gap between AI innovation and real-world energy challenges, paving the way for the development of more intelligent, adaptive, and sustainable energy infrastructures.

This Special Issue aims to advance research in the following areas:

  • Modelling complex systems.
  • Diagnosis and Fault Identification.
  • Updating conventional systems.
  • The development of novel intelligent control topologies and methodologies.
  • Process and method improvement.
  • The applications of intelligent systems to industrial operations.
  • Optimizing and increasing system performance.
  • The uses of intelligent systems.
  • Applications for intelligent controls.
  • Applications for smart grids and micro-grids.
  • Applications for electro-mobility and mobility.
  • Applied power electronics.
  • The Internet of Things.

Dr. Jose Luis Calvo-Rolle
Dr. Francisco Zayas-Gato
Dr. Esteban Pérez
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 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. 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

  • modelling
  • power energy systems
  • fault detection
  • diagnosis
  • control
  • intelligent systems
  • industrial electronics
  • smart grid
  • energy storage

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Published Papers (1 paper)

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Research

20 pages, 3636 KB  
Article
A Hybrid VMD-SSA-LSTM Framework for Short-Term Wind Speed Prediction Based on Wind Farm Measurement Data
by Ruisheng Feng, Bin Fu, Hanxi Xiao, Xu Wang, Maoyu Zhang, Shuqin Zheng, Yanru Wang, Tingjun Xu and Lei Zhou
Energies 2026, 19(2), 517; https://doi.org/10.3390/en19020517 - 20 Jan 2026
Viewed by 518
Abstract
Aiming at the nonlinear and non-stationary characteristics of wind speed series, this study proposes a hybrid forecasting framework that integrates Variational Mode Decomposition (VMD), Sparrow Search Algorithm (SSA), and Long Short-Term Memory (LSTM) networks. First, VMD is employed to adaptively decompose the original [...] Read more.
Aiming at the nonlinear and non-stationary characteristics of wind speed series, this study proposes a hybrid forecasting framework that integrates Variational Mode Decomposition (VMD), Sparrow Search Algorithm (SSA), and Long Short-Term Memory (LSTM) networks. First, VMD is employed to adaptively decompose the original wind speed series into multiple Intrinsic Mode Functions (IMFs) with distinct frequency features, thereby reducing the non-stationarity of the original sequence. Second, SSA is utilized to adaptively optimize key parameters of the LSTM network, including the number of hidden units, learning rate, and dropout rate, to enhance the model’s capability in capturing complex temporal patterns. Finally, the predictions from all modal components are integrated to generate the final wind speed forecast. Experimental results based on 10-min resolution measured data from a coastal wind farm in southeastern China in June 2020 show that the model achieves a Root Mean Square Error (RMSE) of 0.208, a Mean Absolute Error (MAE) of 0.161, and a Mean Absolute Percentage Error (MAPE) of 3.284% on the test set, with its comprehensive performance significantly surpassing benchmark models such as LSTM, VMD-LSTM, MLP, XGBoost, and ARIMA. The limitations of this study mainly include the use of only one month of data for validation, the lack of segmented performance analysis across different wind speed regimes, and a fixed prediction horizon of 10 min. The results indicate that the proposed hybrid forecasting framework provides an effective approach with practical engineering potential for ultra-short-term wind power prediction. Full article
(This article belongs to the Special Issue Artificial Intelligence-Based Approaches for Power Energy System Modelling)
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