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Machines
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Advances in Electrical Power System Design and Artificial Intelligence
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Advances in Electrical Power System Design and Artificial Intelligence

A special issue of Machines (ISSN 2075-1702). This special issue belongs to the section "Electromechanical Energy Conversion Systems".

Deadline for manuscript submissions: 30 September 2025 | Viewed by 1908

Special Issue Editors

Dr. Saddam Aziz

E-Mail Website
Guest Editor
Department of Electrical and Electronic Engineering, The Hong Kong Polytechnic University, Hong Kong
Interests: renewable energy integration; cyber-physical power system; artificial intelligence
Special Issues, Collections and Topics in MDPI journals
Dr. Sadiq Ahmad

E-Mail Website
Guest Editor
Electrical and Computer Engineering, COMSATS University Islamabad, Islamabad 45550, Pakistan
Interests: smart grid technology; power system; energy management and trading; blockchain technology
Dr. Raziq Yaqoob

E-Mail Website
Guest Editor
Department of Electrical Engineering and Computer Science, Alabama A&M University, Huntsville, AL 35762, USA
Interests: cybersecurity; wireless communication; smart grid

Special Issue Information

Dear Colleagues,

This Special Issue aims to archive advanced mathematical methodologies for AI in the design and optimization of modern machinery used in power systems. It investigates how advanced mathematical theories can be incorporated with AI to empower, optimize and enhance the reliability of machinery across various application areas such as power systems. Given the rapid development in both fields, the Special Issue provides an opportunity for academics to disseminate their findings and promote interdisciplinary advancements, welcoming papers on topics including AI-driven machinery optimization and control, prediction-based maintenance using ML, AI in machinery with renewable energy systems, smart grid technologies, real-time data processing, modeling and simulation of machinery systems, control systems based on fuzzy logic and neural networks, AI-driven automation, machine learning algorithms for diagnostics, electromechanical energy conversion and game theory in machinery applications. This Special Issue seeks contributions from researchers and industry professionals, aiming to accelerate the development of intelligent machinery systems by combining mathematics and AI. It invites original research articles, review papers and case studies that present novel applications of mathematical methodologies in AI for modern machinery, including gears, highlighting fresh theoretical approaches, innovative concepts and industry experiences.

Dr. Saddam Aziz
Dr. Sadiq Ahmad
Dr. Raziq Yaqoob
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. Machines is an international peer-reviewed open access monthly 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

  • electrical power systems
  • artificial intelligence
  • machine learning
  • optimization
  • control systems
  • predictive maintenance
  • fault detection
  • renewable energy integration
  • smart grids
  • data analytics
  • energy efficiency
  • sustainability
  • power system reliability
  • intelligent systems
  • grid modernization

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Further information on MDPI's Special Issue policies can be found here.

Published Papers (1 paper)

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Research

18 pages, 4583 KiB  
Article
Solar Irradiance Prediction Method for PV Power Supply System of Mobile Sprinkler Machine Using WOA-XGBoost Model
by Dan Li, Jiwei Qu, Delan Zhu and Zheyu Qin
Machines 2024, 12(11), 804; https://doi.org/10.3390/machines12110804 - 13 Nov 2024
Viewed by 840
Abstract
Solar energy can mitigate the power supply shortage in remote regions for portable irrigation systems. The accurate prediction of solar irradiance is crucial for determining the power capacity of photovoltaic power generation (PVPG) systems for mobile sprinkler machines. In this study, a prediction [...] Read more.
Solar energy can mitigate the power supply shortage in remote regions for portable irrigation systems. The accurate prediction of solar irradiance is crucial for determining the power capacity of photovoltaic power generation (PVPG) systems for mobile sprinkler machines. In this study, a prediction method is proposed to estimate the solar irradiance of typical irrigation areas. The relation between meteorological parameters and solar irradiance is studied, and four different parameter combinations are formed and considered as inputs to the prediction model. Based on meteorological data provided by ten typical radiation stations uniformly distributed nationwide, an Extreme Gradient Boosting (XGBoost) model optimized using the Whale Optimization Algorithm (WOA) is developed to predict solar radiation. The prediction accuracy and stability of the proposed method are then evaluated for different input parameters through training and testing. The differences between the prediction performances of models trained based on single-station data and mixed data from multiple stations are also compared. The obtained results show that the proposed model achieves the highest prediction accuracy when the maximum temperature, minimum temperature, sunshine hours ratio, relative humidity, wind speed, and extraterrestrial radiation are used as input parameters. In the model testing, the RMSE and MAE of WOA-XGBoost are 2.142 MJ·m−2·d−1 and 1.531 MJ·m−2·d−1, respectively, while those of XGBoost are 2.298 MJ·m−2·d−1 and 1.598 MJ·m−2·d−1. The prediction effectiveness is also verified based on measured data. The WOA-XGBoost model has higher prediction accuracy than the XGBoost model. The model developed in this study can be applied to forecast solar irradiance in different regions. By inputting the meteorological parameter data specific to a given area, this model can effectively produce accurate solar irradiance predictions for that region. This study provides a foundation for the optimization of the configuration of PVPG systems for mobile sprinkler machines. Full article
(This article belongs to the Special Issue Advances in Electrical Power System Design and Artificial Intelligence)
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