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State-of-the-Art Machine Learning Tools for Energy Systems
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State-of-the-Art Machine Learning Tools for 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: 10 May 2025 | Viewed by 3557

Special Issue Editors

Dr. Claudio Tomazzoli

E-Mail Website
Guest Editor
Department of Computer Science, University of Verona, 37129 Verona, Italy
Interests: artificial intelligence; machine learning; computational intelligence; pattern recognition; energy management; energy saving
Dr. Matteo Cristani

E-Mail Website
Guest Editor
Department of Computer Science, University of Verona, 37129 Verona, Italy
Interests: artificial intelligence; knowledge representation; machine learning; multi-agent systems
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

We are thrilled to announce a new Special Issue of the MDPI Journal “Energies” titled “State-of-the-Art Machine Learning Tools for Energy Systems”. The purpose of this Special Issue is to gather new investigations into several areas related to the usage of machine learning techniques in the broad area of energy systems. We welcome papers on topics from the following non-exclusive list:

  • Machine learning methods for energy management;
  • Artificial intelligence for energy saving;
  • Digital twin applications for energy generation and management;
  • User profiling in energy management;
  • Smart grid algorithms;
  • Edge computing, green computing and the cloud for energy applications;
  • Smart energy storage with machine learning;
  • Benchmarking and social networking for energy saving.

We welcome contributions that include system descriptions, new specific techniques for machine learning energy applications, technology shift paradigms and any novel applicative research in the fields mentioned above.

The aim of this Special Issue is to focus on the research area of machine learning for energy management as this area is developing quickly and we need to increase the number of avenues for contributions in order to elevate the debate on this topic as soon as possible.

Dr. Claudio Tomazzoli
Dr. Matteo Cristani
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
  • energy production
  • energy saving

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Published Papers (3 papers)

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Research

16 pages, 491 KiB  
Article
Optimal Community Energy Storage System Operation in a Multi-Power Consumer System: A Stackelberg Game Theory Approach
by Gyeong Ho Lee, Junghyun Lee, Seong Gon Choi and Jangkyum Kim
Energies 2024, 17(22), 5683; https://doi.org/10.3390/en17225683 - 13 Nov 2024
Cited by 1 | Viewed by 929
Abstract
The proliferation of community energy storage systems (CESSs) necessitates effective energy management to address financial concerns. This paper presents an efficient energy management scheme for heterogeneous power consumers by analyzing various cost factors relevant to the power system. We propose an authority transaction [...] Read more.
The proliferation of community energy storage systems (CESSs) necessitates effective energy management to address financial concerns. This paper presents an efficient energy management scheme for heterogeneous power consumers by analyzing various cost factors relevant to the power system. We propose an authority transaction model based on a multi-leader multi-follower Stackelberg game, demonstrating the existence of a unique Stackelberg equilibrium to determine optimal bidding prices and allocate authority transactions. Our model shows that implementing a CESS can reduce total electricity costs by 16% compared to the conventional case that does not account for authority transactions among CESS users, highlighting its effectiveness in practical power systems. Full article
(This article belongs to the Special Issue State-of-the-Art Machine Learning Tools for Energy Systems)
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15 pages, 2174 KiB  
Article
Boosting the Development and Management of Wind Energy: Self-Organizing Map Neural Networks for Clustering Wind Power Outputs
by Yanqian Li, Yanlai Zhou, Yuxuan Luo, Zhihao Ning and Chong-Yu Xu
Energies 2024, 17(21), 5485; https://doi.org/10.3390/en17215485 - 1 Nov 2024
Viewed by 645
Abstract
Aimed at the information loss problem of using discrete indicators in wind power output characteristics analysis, a self-organizing map neural network-based clustering method is proposed in this study. By identifying the appropriate representativeness and topological structure of the competition layer, cluster analysis of [...] Read more.
Aimed at the information loss problem of using discrete indicators in wind power output characteristics analysis, a self-organizing map neural network-based clustering method is proposed in this study. By identifying the appropriate representativeness and topological structure of the competition layer, cluster analysis of the wind power output process in four seasons is realized. The output characteristics are evaluated through multiple evaluation indicators. Taking the wind power output of the Hunan power grid as a case study, the results underscore that the 1 × 3-dimensional competition layer structure had the highest representativeness (72.9%), and the wind power output processes of each season were divided into three categories, with a robust and stable topology structure. Summer and winter were the most representative seasons. Summer had strong volatility and small wind power outputs, which required the utilization of other power sources to balance power supply and load demand. Winter featured low volatility and large wind power outputs, necessitating cooperation with peak-shaving power sources to enhance the power grid’s absorbability to wind power. The seasonal clustering analysis of wind power outputs will be helpful to analyze the seasonality of wind power outputs and can provide scientific and technical support for guiding the power grid’s operation and management. Full article
(This article belongs to the Special Issue State-of-the-Art Machine Learning Tools for Energy Systems)
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22 pages, 11854 KiB  
Article
Convolutional Neural Network and Bidirectional Long Short-Term Memory (CNN-BiLSTM)-Attention-Based Prediction of the Amount of Silica Powder Moving in and out of a Warehouse
by Dudu Guo, Pengbin Duan, Zhen Yang, Xiaojiang Zhang and Yinuo Su
Energies 2024, 17(15), 3757; https://doi.org/10.3390/en17153757 - 30 Jul 2024
Cited by 1 | Viewed by 1093
Abstract
Raw material inventory control is indispensable for ensuring the cost reduction and efficiency of enterprises. Silica powder is an essential raw material for new energy enterprises. The inventory control of silicon powder is of great concern to enterprises, but due to the complexity [...] Read more.
Raw material inventory control is indispensable for ensuring the cost reduction and efficiency of enterprises. Silica powder is an essential raw material for new energy enterprises. The inventory control of silicon powder is of great concern to enterprises, but due to the complexity of the market environment and the inadequacy of information technology, inventory control of silica powder has been ineffective. One of the most significant reasons for this is that existing methods encounter difficulty in effectively extracting the local and long-term characteristics of the data, which leads to significant errors in forecasting and poor accuracy. This study focuses on improving the accuracy of corporate inventory forecasting. We propose an improved CNN-BiLSTM-attention prediction model that uses convolutional neural networks (CNNs) to extract the local features from a dataset. The attention mechanism (attention) uses the point multiplication method to weigh the acquired features and the bidirectional long short-term memory (BiLSTM) network to acquire the long-term features of the dataset. The final output of the model is the predicted value of silica powder and the evaluation metrics. The proposed model is compared with five other models: CNN, LSTM, CNN-LSTM, CNN-BiLSTM, and CNN-LSTM-attention. The experiments show that the improved CNN-BiLSTM-attention prediction model can predict inbound and outbound silica powder very well. The accuracy of the prediction of the inbound test set is higher than that of the other five models by 7.429%, 11.813%, 15.365%, 10.331%, and 5.821%, respectively. The accuracy of the outbound storage prediction is higher than that of the other five models by 14.535%, 15.135%, 1.603%, 7.584%, and 18.784%, respectively. Full article
(This article belongs to the Special Issue State-of-the-Art Machine Learning Tools for Energy Systems)
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