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Energies
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Advances in Machine Learning Applications in Modern Energy System
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Advances in Machine Learning Applications in Modern Energy System

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: closed (3 June 2024) | Viewed by 3179

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 the field of application. Further requirements, like environment impact or energy awareness, are also considered, which led to a transformation of the design into more complex systems such as smart grids, renewable energy systems or building management systems. This transformation involves changes in technologies 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 able to handle and extract information from data successfully possible. 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. The application in energy systems can provide additional features to make them more effective; for instance, a better knowledge of the system, support decisions for an effective energy management or early fault detection.

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

  • 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 (2 papers)

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21 pages, 3426 KiB  
Article
Short-Term Electric Load Forecasting Based on Signal Decomposition and Improved TCN Algorithm
by Xinjian Xiang, Tianshun Yuan, Guangke Cao and Yongping Zheng
Energies 2024, 17(8), 1815; https://doi.org/10.3390/en17081815 - 10 Apr 2024
Cited by 1 | Viewed by 1193
Abstract
In the realm of power systems, short-term electric load forecasting is pivotal for ensuring supply–demand balance, optimizing generation planning, reducing operational costs, and maintaining grid stability. Short-term load curves are characteristically coarse, revealing high-frequency data upon decomposition that exhibit pronounced non-linearity and significant [...] Read more.
In the realm of power systems, short-term electric load forecasting is pivotal for ensuring supply–demand balance, optimizing generation planning, reducing operational costs, and maintaining grid stability. Short-term load curves are characteristically coarse, revealing high-frequency data upon decomposition that exhibit pronounced non-linearity and significant noise, complicating efforts to enhance forecasting precision. To address these challenges, this study introduces an innovative model. This model employs complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) to bifurcate the original load data into low- and high-frequency components. For the smoother low-frequency data, a temporal convolutional network (TCN) is utilized, whereas the high-frequency components, which encapsulate detailed load history information yet suffer from a lower fitting accuracy, are processed using an enhanced soft thresholding TCN (SF-TCN) optimized with the slime mould algorithm (SMA). Experimental tests of this methodology on load forecasts for the forthcoming 24 h across all seasons have demonstrated its superior forecasting accuracy compared to that of non-decomposed models, such as support vector regression (SVR), recurrent neural network (RNN), gated recurrent unit (GRU), long short-term memory (LSTM), convolutional neural network-LSTM (CNN-LSTM), TCN, Informer, and decomposed models, including CEEMDAN-TCN and CEEMDAN-TCN-SMA. Full article
(This article belongs to the Special Issue Advances in Machine Learning Applications in Modern Energy System)
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Review

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23 pages, 4091 KiB  
Review
Data-Driven Approaches for Energy Theft Detection: A Comprehensive Review
by Soohyun Kim, Youngghyu Sun, Seongwoo Lee, Joonho Seon, Byungsun Hwang, Jeongho Kim, Jinwook Kim, Kyounghun Kim and Jinyoung Kim
Energies 2024, 17(12), 3057; https://doi.org/10.3390/en17123057 - 20 Jun 2024
Cited by 2 | Viewed by 1401
Abstract
The transition to smart grids has served to transform traditional power systems into data-driven power systems. The purpose of this transition is to enable effective energy management and system reliability through an analysis that is centered on energy information. However, energy theft caused [...] Read more.
The transition to smart grids has served to transform traditional power systems into data-driven power systems. The purpose of this transition is to enable effective energy management and system reliability through an analysis that is centered on energy information. However, energy theft caused by vulnerabilities in the data collected from smart meters is emerging as a primary threat to the stability and profitability of power systems. Therefore, various methodologies have been proposed for energy theft detection (ETD), but many of them are challenging to use effectively due to the limitations of energy theft datasets. This paper provides a comprehensive review of ETD methods, highlighting the limitations of current datasets and technical approaches to improve training datasets and the ETD in smart grids. Furthermore, future research directions and open issues from the perspective of generative AI-based ETD are discussed, and the potential of generative AI in addressing dataset limitations and enhancing ETD robustness is emphasized. Full article
(This article belongs to the Special Issue Advances in Machine Learning Applications in Modern Energy System)
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