Mathematical Modelling and Numerical Analysis in Electrical Engineering, 3rd Edition

A special issue of Mathematics (ISSN 2227-7390). This special issue belongs to the section "E: Applied Mathematics".

Deadline for manuscript submissions: 31 March 2026 | Viewed by 995

Special Issue Editors


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Guest Editor
Department of Electrical Engineering, Tshwane University of Technology, Pretoria 0183, South Africa
Interests: electrical machines; power engineering; renewable energy
Special Issues, Collections and Topics in MDPI journals

E-Mail Website
Guest Editor
Department of Electrical and Electronic Engineering, College of Engineering and Engineering Technology (CEET), Michael Okpara University of Agriculture Umudike, Umuahia 440001, Abia, Nigeria
Interests: electrical power; machines
Special Issues, Collections and Topics in MDPI journals

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Guest Editor
Groupe de Recherche en Electrotechnique et Automatique du Havre (GREAH), Université Le Havre Normandie (ULHN), 76600 Le Havre, France
Interests: electrical machines; modelling, design, analysis, control, radial field, and axial field rotating machines; flat and tubular linear machines; hybrid excited synchronous machines; flux switching structures; transport applications; renewable energy applications
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Mathematics and electrical engineering have always existed mutually. Mathematics is the science of studying numbers, quantities, geometry, and shapes, while electrical engineering deals with, among others, the practical application of mathematical theory in circuit design, electromagnetics, and electronics. To bridge the gap between mathematical problems and real-world solutions, numerical processes have evolved into solving complex mathematical models based on high-end computation. We kindly invite you to submit your research articles to our Special Issue titled “Mathematical Modelling and Numerical Analysis in Electrical Engineering, 3rd Edition”. The scope of this Special Issue will cover mathematical methods and techniques in electrical engineering, including analytical, semi-numerical, and numerical-based computational modeling and analyses of electrical engineering problems, as well as mathematical and numerical designs for industrial-based electrical engineering devices and systems.

Suggested Topics for this Special Issue include the following:

  • Numerical and analytical methods and simulation of electromagnetic fields, devices, and systems;
  • Mathematical and numerical modeling in electrical power engineering;
  • Computational techniques for efficient numerical analysis of electrical devices and networks;
  • Fast numerical modeling and analysis techniques for prototyping of electrical machines;
  • Mathematical and numerical processes in power system and electrical machine optimization;
  • Applied mathematics in power engineering theory and design;
  • Thermal analysis and control of electrical machines based on mathematical modeling and simulations;
  • Finite element analyses for industrial design feasibility of renewable energy devices.

We look forward to receiving your contributions.

Dr. Udochukwu B. Akuru
Prof. Dr. Ogbonnaya I. Okoro
Prof. Dr. Yacine Amara
Guest Editors

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Keywords

  • applied mathematics
  • finite element analysis
  • numerical modeling and analysis
  • analytical modeling
  • electrical power engineering
  • electrical machines
  • electrical networks
  • renewable energy devices
  • design optimization
  • power systems
  • electromagnetic fields
  • mathematical modeling
  • computer-aided design and modeling
  • industrial design and prototyping

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Published Papers (1 paper)

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Research

33 pages, 6266 KB  
Article
Numerical Optimization of Neuro-Fuzzy Models Using Evolutionary Algorithms for Electricity Demand Forecasting in Pre-Tertiary Institutions
by Stephen O. Oladipo, Udochukwu B. Akuru and Ogbonnaya I. Okoro
Mathematics 2025, 13(16), 2648; https://doi.org/10.3390/math13162648 - 18 Aug 2025
Viewed by 801
Abstract
Reliable electricity supply in educational facilities demands predictive frameworks that reflect usage patterns and consumption variability. This study investigates electricity-consumption forecasting in lower-to-middle-income pre-tertiary institutions in Western Cape, South Africa, using adaptive neuro-fuzzy inference systems (ANFISs) optimized by evolutionary algorithms. Using genetic algorithm [...] Read more.
Reliable electricity supply in educational facilities demands predictive frameworks that reflect usage patterns and consumption variability. This study investigates electricity-consumption forecasting in lower-to-middle-income pre-tertiary institutions in Western Cape, South Africa, using adaptive neuro-fuzzy inference systems (ANFISs) optimized by evolutionary algorithms. Using genetic algorithm (GA) and particle swarm optimization (PSO) algorithms, the impact of two clustering techniques, Subtractive Clustering (SC) and Fuzzy C-Means (FCM), along with their cogent hyperparameters, were investigated, yielding several sub-models. The efficacy of the proposed models was evaluated using five standard statistical metrics, while the optimal model was also compared with other variants and hybrid models. Results obtained showed that the GA-ANFIS-FCM with four clusters achieved the best performance, recording the lowest Root Mean Square Error (RMSE) of 3.83, Mean Absolute Error (MAE) of 2.40, Theil’s U of 0.87, and Standard Deviation (SD) of 3.82. The developed model contributes valuable insights towards informed energy decisions. Full article
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