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Advances in Machine Learning Applications in Modern Energy Systems: 2nd Edition

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: 25 August 2025 | Viewed by 5551

Special Issue Editors

Dr. Daniel Pérez-López

E-Mail Website
Guest Editor
Suppress Research Group, Escuela de Ingenierías, Universidad de León, 24007 Leon, Spain
Interests: machine learning; visual analytics; industrial processes
Special Issues, Collections and Topics in MDPI journals
Dr. Fernando Sánchez Lasheras

E-Mail Website
Guest Editor
Department of Mathematics, Faculty of Sciences, Oviedo University, Calle Leopoldo Calvo Sotelo 18, 33007 Oviedo, Spain
Interests: machine learning; deep learning; atmospheric turbulence; astronomy; adaptive optics; solar observation
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Energy systems are designed in diverse ways depending on their field of application. Further requirements, like environment impact or energy awareness, can also be considered, as they transform the design into more complex systems such as smart grids, renewable energy systems, and building management systems. This transformation involves changes in technology, providing more resources for energy management and also challenges for many research activities.

Several components are included in these systems to facilitate the collection of energy data, and the current computation capabilities make machine learning applications capable of handling and extracting information from data successfully. Many machine learning methods exist, ranging from neural networks, deep learning, and ensemble models to hybrid solutions that attend to the problems present in these complex systems. Their application in energy systems can provide additional features to make them more effective; for instance, having better knowledge of the system, making support decisions for effective energy management, or achieving early fault detection.

Topics of interest for publication include, but are not limited to, the following:

  • Energy estimation;
  • Prediction models;
  • Energy system modeling;
  • Load forecasting;
  • Condition monitoring;
  • Energy disaggregation;
  • Fault detection;
  • Optimization;
  • Control.

Dr. Daniel Pérez-López
Dr. Fernando Sánchez Lasheras
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

  • machine learning
  • energy systems
  • applications
  • modeling

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Published Papers (5 papers)

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Research

29 pages, 5846 KiB  
Article
Explainable AI-Driven Quantum Deep Neural Network for Fault Location in DC Microgrids
by Amir Hossein Poursaeed and Farhad Namdari
Energies 2025, 18(4), 908; https://doi.org/10.3390/en18040908 - 13 Feb 2025
Cited by 1 | Viewed by 829
Abstract
Fault location in DC microgrids (DCMGs) is a critical challenge due to the system’s inherent complexities and the demand for high reliability in modern power systems. This study proposes an explainable artificial intelligence (XAI)-based quantum deep neural network (QDNN) framework to address fault [...] Read more.
Fault location in DC microgrids (DCMGs) is a critical challenge due to the system’s inherent complexities and the demand for high reliability in modern power systems. This study proposes an explainable artificial intelligence (XAI)-based quantum deep neural network (QDNN) framework to address fault localization challenges in DCMGs. First, voltage signals from the DCMG are collected and analyzed using high-order synchrosqueezing transform to detect traveling waves (TWs) and extract critical fault parameters such as time of arrival, magnitude, and polarity of the first and second TWs. These features are fed into the proposed QDNN model that integrates advanced learning techniques for accurate fault localization. The cumulative distance from the fault point to the bus connecting the DCMG to the power network is considered the output vector. The model uses a combination of deep learning and quantum computing techniques to extract features and improve accuracy. To ensure transparency, an XAI technique called Shapley additive explanations (SHAP) is applied, enabling system operators to identify critical fault features. The SHAP-based explainability framework plays a critical role in translating the model’s predictions into actionable insights, ensuring that the proposed solution is not only accurate but also practically implementable in real-world scenarios. The results demonstrate the QDNN framework’s superior accuracy in fault localization even in noisy environments and with high-resistance faults, independent of voltage levels and DCMG configurations, making it a robust solution for modern power systems. Full article
(This article belongs to the Special Issue Advances in Machine Learning Applications in Modern Energy Systems: 2nd Edition)
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21 pages, 758 KiB  
Article
A Diffusion–Attention-Enhanced Temporal (DATE-TM) Model: A Multi-Feature-Driven Model for Very-Short-Term Household Load Forecasting
by Yitao Zhao, Jiahao Li, Chuanxu Chen and Quansheng Guan
Energies 2025, 18(3), 486; https://doi.org/10.3390/en18030486 - 22 Jan 2025
Cited by 1 | Viewed by 611
Abstract
With the proliferation of smart home devices and the ever-increasing demand for household energy management, very-short-term load forecasting (VSTLF) has become imperative for energy usage optimization, cost saving and for sustaining grid stability. Despite recent advancements, VSTLF in the household scenario still poses [...] Read more.
With the proliferation of smart home devices and the ever-increasing demand for household energy management, very-short-term load forecasting (VSTLF) has become imperative for energy usage optimization, cost saving and for sustaining grid stability. Despite recent advancements, VSTLF in the household scenario still poses challenges. For instance, some characteristics (e.g., high-frequency, noisy and non-stationary) exacerbate the data processing and model training procedures, and the heterogeneity in household consumption patterns causes difficulties for models with the generalization capability. Further, the real-time data processing requirement calls for both the high forecasting accuracy and improved computational efficiency. Thus, we propose a diffusion–attention-enhanced temporal (DATE-TM) model with multi-feature fusion to address the above issues. First, the DATE-TM model could integrate residents’ electricity consumption patterns with climatic factors. Then, it extracts the temporal feature using an encoder and meanwhile models the data uncertainty through a diffusion model. Finally, the decoder, enhanced with the attention mechanism, creates the precise prediction for the household load forecasting. Experimental results reveal that DATE-TM significantly surpasses classical neural networks such as BiLSTM and DeepAR, especially in handling the data uncertainty and long-term dependency. Full article
(This article belongs to the Special Issue Advances in Machine Learning Applications in Modern Energy Systems: 2nd Edition)
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23 pages, 5045 KiB  
Article
LSTM vs. Prophet: Achieving Superior Accuracy in Dynamic Electricity Demand Forecasting
by Saleh Albahli
Energies 2025, 18(2), 278; https://doi.org/10.3390/en18020278 - 10 Jan 2025
Cited by 2 | Viewed by 1995
Abstract
Accurate electricity demand forecasting is critical for improving energy efficiency, maintaining grid stability, reducing operational costs, and promoting sustainability. This study presents a novel hybrid forecasting model that integrates Long Short-Term Memory (LSTM) networks and Prophet models, leveraging their complementary strengths through a [...] Read more.
Accurate electricity demand forecasting is critical for improving energy efficiency, maintaining grid stability, reducing operational costs, and promoting sustainability. This study presents a novel hybrid forecasting model that integrates Long Short-Term Memory (LSTM) networks and Prophet models, leveraging their complementary strengths through a dynamic weighted ensemble methodology. The LSTM component captures nonlinear dependencies and long-term temporal patterns, while Prophet models seasonal trends and event-driven fluctuations. The hybrid model was evaluated using a comprehensive dataset of hourly electricity consumption from Ontario, Canada, achieving a Root Mean Square Error (RMSE) of 65.34, Mean Absolute Percentage Error (MAPE) of 7.3%, and an R2 of 0.98. These results demonstrate significant improvements over standalone LSTM, Prophet, and other State-of-the-Art methods, highlighting the hybrid model’s adaptability and superior accuracy. This study underscores the practical implications of the hybrid approach, particularly in energy grid management and resource optimization, setting a new benchmark for time series forecasting in the energy sector. Full article
(This article belongs to the Special Issue Advances in Machine Learning Applications in Modern Energy Systems: 2nd Edition)
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23 pages, 1657 KiB  
Article
Outlier Detection and Correction in Smart Grid Energy Demand Data Using Sparse Autoencoders
by Levi da Costa Pimentel, Ricardo Wagner Correia Guerra Filho, Juan Moises Mauricio Villanueva and Yuri Percy Molina Rodriguez
Energies 2024, 17(24), 6403; https://doi.org/10.3390/en17246403 - 19 Dec 2024
Viewed by 830
Abstract
The implementation of smart grids introduces complexities where data quality issues, particularly outliers, pose significant challenges to accurate data analysis. This work develops an integrated methodology for the detection and correction of outliers in energy demand data, based on Artificial Neural Network autoencoders. [...] Read more.
The implementation of smart grids introduces complexities where data quality issues, particularly outliers, pose significant challenges to accurate data analysis. This work develops an integrated methodology for the detection and correction of outliers in energy demand data, based on Artificial Neural Network autoencoders. The proposed approach is submitted across multiple scenarios using real-world data from a substation, where the influence of the variation in the number of outliers present in the database is evaluated, as well as the variation in their amplitudes on the functioning of the algorithms. The results provide an overview of the operation as well as demonstrate the effectiveness of the proposed methodology that manages to improve some indices achieved by previous works, reaching accuracy and F-score superior to 99% and 97%, respectively, for the detection algorithm, as well as a square root mean squared error (RMSE) and a mean absolute percentage error (MAPE) of less than 0.2 MW and 2%, respectively. Full article
(This article belongs to the Special Issue Advances in Machine Learning Applications in Modern Energy Systems: 2nd Edition)
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31 pages, 1942 KiB  
Article
An Evidential Solar Irradiance Forecasting Method Using Multiple Sources of Information
by Mohamed Mroueh, Moustapha Doumiati, Clovis Francis and Mohamed Machmoum
Energies 2024, 17(24), 6361; https://doi.org/10.3390/en17246361 - 18 Dec 2024
Viewed by 724
Abstract
In the context of global warming, renewable energy sources, particularly wind and solar power, have garnered increasing attention in recent decades. Accurate forecasting of the energy output in microgrids (MGs) is essential for optimizing energy management, reducing maintenance costs, and prolonging the lifespan [...] Read more.
In the context of global warming, renewable energy sources, particularly wind and solar power, have garnered increasing attention in recent decades. Accurate forecasting of the energy output in microgrids (MGs) is essential for optimizing energy management, reducing maintenance costs, and prolonging the lifespan of energy storage systems. This study proposes an innovative approach to solar irradiance forecasting based on the theory of belief functions, introducing a novel and flexible evidential method for short-to-medium-term predictions. The proposed machine learning model is designed to effectively handle missing data and make optimal use of available information. By integrating multiple predictive models, each focusing on different meteorological factors, the approach enhances forecasting accuracy. The Yager combination method and pignistic transformation are utilized to aggregate the individual models. Applied to a publicly available dataset, the method achieved promising results, with an average root mean square error (RMS) of 27.83 W/m2 calculated from eight distinct forecast days. This performance surpasses the best reported results of 30.21 W/m2 from recent comparable studies for one-day-ahead solar irradiance forecasting. Comparisons with deep learning-based methods, such as long short-term memory (LSTM) networks and recurrent neural networks (RNNs), demonstrate that the proposed approach is competitive with state-of-the-art techniques, delivering reliable predictions with significantly less training data. The full potential and limitations of the proposed approach are also discussed. Full article
(This article belongs to the Special Issue Advances in Machine Learning Applications in Modern Energy Systems: 2nd Edition)
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