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Applied Sciences
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Applications of Neural Network Modeling in Distribution Network
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Applications of Neural Network Modeling in Distribution Network

A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Energy Science and Technology".

Deadline for manuscript submissions: closed (31 July 2023) | Viewed by 4287

Special Issue Editors

Prof. Dr. Almoataz Youssef Abdelaziz

E-Mail Website
Guest Editor
Electrical Power & Machines Department, Faculty of Engineering, Ain Shams University, Cairo 11517, Egypt
Interests: applications of artificial intelligence, evolutionary and heuristic optimization techniques to power system planning, operation, and control
Special Issues, Collections and Topics in MDPI journals
Dr. Ahmed F. Zobaa

E-Mail Website
Guest Editor
Electronic and Electrical Engineering Department, Brunel University London, London UB8 3PH, UK
Interests: lighting applications; power quality problems in power systems; grid integration of (marine) renewable energy; design; performance analysis; and cost benefit analysis of (marine) renewable energy systems; energy management; energy systems; smart grids
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The applications of classical neural networks in electrical distribution systems with renewable energy resources are considered a promising research topic. The nature of the uncertainty of distributed generation (wind and solar) and vehicle-to-grid technology is considered a significant challenge in the application of classical neural networks to state estimation and state forecasting in distribution networks.

This Special Issue aims to cover the most recent advancements in the application of conventional neural networks and deep-learning-based models to mitigate the aforementioned challenges in distribution systems with renewable energy sources, thus collecting innovative and original studies as well as literature reviews. The topics and themes of this Special Issue can include, but are not limited to:

  • The application of deep neural networks to electrical distribution system state estimation and forecasting;
  • The application of artificial neural networks in analyzing and studying daily electrical loads;
  • The application of artificial neural networks to network reconfiguration for power loss minimization in distribution networks;
  • The application of artificial neural networks to predict the output power of different types of photovoltaic cells;
  • The modeling and optimization of wind turbine power using artificial neural networks; 
  • The application of artificial neural networks to predict electric vehicle charging demand;
  • Artificial intelligence techniques to control a proton exchange membrane fuel cell system;
  • The development and application of optimization techniques in distribution systems;
  • Forecasting methods for energy prices and smart grid applications.

Prof. Dr. Almoataz Youssef Abdelaziz
Dr. Ahmed F. Zobaa
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. Applied Sciences 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 2400 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

  • artificial neural networks
  • multilayer perceptron networks
  • convolutional neural networks
  • long short-term memory networks
  • forecasting
  • distribution systems
  • renewable energy
  • wind
  • solar
  • electric vehicle
  • optimization
  • smart grids

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Published Papers (1 paper)

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Review

43 pages, 9293 KiB  
Review
A Review on Neural Network Based Models for Short Term Solar Irradiance Forecasting
by Abbas Mohammed Assaf, Habibollah Haron, Haza Nuzly Abdull Hamed, Fuad A. Ghaleb, Sultan Noman Qasem and Abdullah M. Albarrak
Appl. Sci. 2023, 13(14), 8332; https://doi.org/10.3390/app13148332 - 19 Jul 2023
Cited by 13 | Viewed by 3829
Abstract
The accuracy of solar energy forecasting is critical for power system planning, management, and operation in the global electric energy grid. Therefore, it is crucial to ensure a constant and sustainable power supply to consumers. However, existing statistical and machine learning algorithms are [...] Read more.
The accuracy of solar energy forecasting is critical for power system planning, management, and operation in the global electric energy grid. Therefore, it is crucial to ensure a constant and sustainable power supply to consumers. However, existing statistical and machine learning algorithms are not reliable for forecasting due to the sporadic nature of solar energy data. Several factors influence the performance of solar irradiance, such as forecasting horizon, weather classification, and performance evaluation metrics. Therefore, we provide a review paper on deep learning-based solar irradiance forecasting models. These models include Long Short-Term Memory (LTSM), Gated Recurrent Unit (GRU), Recurrent Neural Network (RNN), Convolutional Neural Network (CNN), Generative Adversarial Networks (GAN), Attention Mechanism (AM), and other existing hybrid models. Based on our analysis, deep learning models perform better than conventional models in solar forecasting applications, especially in combination with some techniques that enhance the extraction of features. Furthermore, the use of data augmentation techniques to improve deep learning performance is useful, especially for deep networks. Thus, this paper is expected to provide a baseline analysis for future researchers to select the most appropriate approaches for photovoltaic power forecasting, wind power forecasting, and electricity consumption forecasting in the medium term and long term. Full article
(This article belongs to the Special Issue Applications of Neural Network Modeling in Distribution Network)
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