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Advanced Applications of Machine Learning and Artificial Intelligence in Smart...
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Advanced Applications of Machine Learning and Artificial Intelligence in Smart Grids

A special issue of Energies (ISSN 1996-1073). This special issue belongs to the section "A1: Smart Grids and Microgrids".

Deadline for manuscript submissions: closed (10 November 2023) | Viewed by 1973

Special Issue Editors

Dr. Andrea Fioravanti

E-Mail Website
Guest Editor
Department of Industrial and Information Engineering and Economics, University of L’Aquila, 67100 L’Aquila, Italy
Interests: power system resilience; power quality; IoT applications; analysis of electrical systems with innovative soft computing and artificial intelligence techniques
Dr. Fabrizio Ciancetta

E-Mail Website
Guest Editor
Department of Industrial and Information Engineering and Economics (DIIIE), University of L’Aquila, L’Aquila, Italy
Interests: smart system; fault diagnostics; measurement and monitoring system; distributed measurement system; signal processing; multilevel inverter; IoT system; real-time system; data processing
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Advanced technologies are employed by smart grids to enhance the efficiency, dependability, security, and sustainability of the electricity system. This Special Issue solicits contributions that demonstrate the use of artificial intelligence and machine learning methods to address issues concerning smart grid administration, which comprises managing energy demand forecasting, renewable energy generation, the distribution network, and system security. This Special Issue encourages authors to share their results, analyses, studies and critical evaluations that focus on the integration of machine learning and artificial intelligence algorithms into smart grids. Contributions that illustrate the practical application of methods such as neural networks, data analysis, clustering algorithms, and supervised and unsupervised learning are particularly welcome.

This Special Issue’s topics of interest include, but are not limited to, the following:

  • Machine learning and AI systems for stability, reliability and to improve the resilience of the electricity system;
  • Protection systems for Smart Grids, for example, utilizing data from PMU;
  • Intelligent systems for energy storage with the integration of the "Vehicle 2 Grid" systems;
  • Development of diagnostic systems for smart grids, alongside the integration of big data, cloud systems and API tools;
  • ML systems for demand-side management and demand response and building energy management;
  • ICT technologies for power systems;
  • Forecast of load and electricity production.

Dr. Andrea Fioravanti
Dr. Fabrizio Ciancetta
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

  • smart grid
  • machine learning
  • artificial intelligence
  • grid protection
  • distributed energy resources
  • fault diagnosis
  • demand response
  • energy management system

Published Papers (2 papers)

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Research

17 pages, 3727 KiB  
Article
A Deep Neural Network-Based Optimal Scheduling Decision-Making Method for Microgrids
by Fei Chen, Zhiyang Wang and Yu He
Energies 2023, 16(22), 7635; https://doi.org/10.3390/en16227635 - 17 Nov 2023
Viewed by 685
Abstract
With the rapid growth in the proportion of renewable energy access and the structural complexity of distributed energy systems, traditional microgrid (MG) scheduling methods that rely on mathematical optimization models and expert experience are facing significant challenges. Therefore, it is essential to present [...] Read more.
With the rapid growth in the proportion of renewable energy access and the structural complexity of distributed energy systems, traditional microgrid (MG) scheduling methods that rely on mathematical optimization models and expert experience are facing significant challenges. Therefore, it is essential to present a novel scheduling technique with high intelligence and fast decision-making capacity to realize MGs’ automatic operation and regulation. This paper proposes an optimal scheduling decision-making method for MGs based on deep neural networks (DNN). Firstly, a typical mathematical scheduling model used for MG operation is introduced, and the limitations of current methods are analyzed. Then, a two-stage optimal scheduling framework comprising day-ahead and intra-day stages is presented. The day-ahead part is solved by mixed integer linear programming (MILP), and the intra-day part uses a convolutional neural network (CNN)—bidirectional long short-term memory (Bi LSTM) for high-speed rolling decision making, with the outputs adjusted by a power correction balance algorithm. Finally, the validity of the model and algorithm of this paper are verified by arithmetic case analysis. Full article
(This article belongs to the Special Issue Advanced Applications of Machine Learning and Artificial Intelligence in Smart Grids)
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13 pages, 2303 KiB  
Article
Named Entity Identification in the Power Dispatch Domain Based on RoBERTa-Attention-FL Model
by Yan Chen, Dezhao Lin, Qi Meng, Zengfu Liang and Zhixiang Tan
Energies 2023, 16(12), 4654; https://doi.org/10.3390/en16124654 - 12 Jun 2023
Cited by 1 | Viewed by 954
Abstract
Named entity identification is an important step in building a knowledge graph of the grid domain, which contains a certain number of nested entities. To address the issue of nested entities in the Chinese power dispatching domain’s named entity recognition, we propose a [...] Read more.
Named entity identification is an important step in building a knowledge graph of the grid domain, which contains a certain number of nested entities. To address the issue of nested entities in the Chinese power dispatching domain’s named entity recognition, we propose a RoBERTa-Attention-FL model. This model effectively recognizes nested entities using the span representation annotation method. We extract the output values from RoBERTa’s middle 4–10 layers, obtain syntactic information from the Transformer Encoder layers via the multi-head self-attention mechanism, and integrate it with deep semantic information output from RoBERTa’s last layer. During training, we use Focal Loss to mitigate the sample imbalance problem. To evaluate the model’s performance, we construct named entity recognition datasets for flat and nested entities in the power dispatching domain annotated with actual power operation data, and conduct experiments. The results indicate that compared to the baseline model, the RoBERTa-Attention-FL model significantly improves recognition performance, increasing the F1-score by 4.28% to 90.35%, with an accuracy rate of 92.53% and a recall rate of 88.12%. Full article
(This article belongs to the Special Issue Advanced Applications of Machine Learning and Artificial Intelligence in Smart Grids)
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