Neural Networks and Deep Learning and Their Applications in Electrical Engineering
A special issue of Energies (ISSN 1996-1073). This special issue belongs to the section "F: Electrical Engineering".
Deadline for manuscript submissions: 31 March 2025 | Viewed by 359
Special Issue Editors
Interests: machine learning; artificial intelligence; deep learning; embedded systems; electric meters; smart meters; low and high-level programming
Special Issues, Collections and Topics in MDPI journals
Interests: pattern recognition (machine learning); digital signal processing; embedded systems and instrumentation
Interests: distributed power generation; power quality; cooperative and multifunctional inverter control; microgrids; smart energy grids and intelligent systems applied to engineering
Special Issue Information
Dear Colleagues,
With the proliferation of smart devices, sensors, and Internet of Things (IoT) applications, electrical systems generate vast amounts of data that can be applied to optimize system performance, predict failures, and improve decision-making processes. With the employment of statistical analysis, artificial intelligence (AI), data visualization techniques, and machine learning algorithms, it is possible to discover patterns, correlations, and anomalies in the data, enabling the design of more efficient and reliable electrical systems. In this sense, artificial intelligence and deep learning become essential tools in modern electrical engineering applications.
Based on your reputation and expertise in the field, we invite you to publish one of your innovative works in our Special Issue entitled “Neural Networks and Deep Learning and Their Applications in Electrical Engineering” in Energies. We expect all the published papers to be widely read and highly influential within the field.
This Special Issue aims to present and discuss innovative works and review papers on the use of artificial neural networks and deep learning in the area of electrical engineering.
In this Special Issue, original research articles and reviews are welcome. Research areas may include (but are not limited to) the following topics: power grid optimization, demand forecasting, equipment fault diagnosis, energy quality control in manufacturing lines, automation and robotics systems, energy efficiency improvement, process optimization, more precise decision making, fault identification and predictive maintenance, and the development of autonomous systems.
Dr. Wesley A. De Souza
Prof. Dr. André Eugênio Lazzaretti
Dr. Augusto Matheus dos Santos Alonso
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
- deep learning
- smart grids
- energy efficiency
- electrical consumption
- power utilities
- distributed generation
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