Guest Editor(s)
Prof. Dr. Francisco José Sánchez Sutil
Email: fssutil@ujaen.es
Affiliation: Department of Electrical Engineering, University of Jaen, EPS Jaen, Spain
Homepage:
Research Interests: IoT for electrical engineering: smart meters, smart power quality anallizers, smart electrical devices, LPWAN, DLR, supra harmonics
Prof. Dr. Antonio Cano Ortega
Email: acano@ujaen.es
Affiliation: Department of Electrical Engineering, University of Jaen, EPS Jaen, Spain
Homepage:
Research Interests: IoT for electrical engineering: smart meters, smart power quality anallizers, smart electrical devices, LPWAN, DLR
Summary
The rapid evolution of electrical engineering is being driven by the convergence of modern computing paradigms and physical infrastructure. Among these, Artificial Intelligence (AI) and Machine Learning (ML) have emerged as pivotal technologies for optimizing, managing, and securing modern electrical systems. As power grids transition toward decentralized, highly variable architectures, traditional control methods often prove insufficient for handling real-time complexity.
This Special Issue focuses on the deployment of AI methodologies across critical domains of modern electrical engineering. Key areas of interest include the integration of renewable energy sources, where machine learning models significantly enhance forecasting accuracy and grid stability. Furthermore, the modern electrical landscape heavily relies on robust IoT communications; hence, low-power wide-area networks (LPWAN) equipped with AI-driven analytics are essential for continuous data acquisition, dynamic load Monitoring, and predictive maintenance. In parallel, the electrification of transportation poses unique challenges to grid infrastructure. AI application in Electric Vehicles (EVs) extends from intelligent battery management systems and autonomous charging schedules to smart microgrid interactions (V2G - Vehicle-to-Grid).
This issue aims to bring together researchers, industry experts, and academics to showcase cutting-edge algorithms, hardware implementations, and real-world deployment strategies that address current efficiency, reliability, and sustainability challenges in electrical engineering through artificial intelligence.
Scope and Key Research Areas:
· Electric Mobility & Smart Charging: AI algorithms for Electric Vehicle (EV) charging optimization, Vehicle-to-Grid (V2G) management, state-of-charge (SoC) estimation, and battery thermal management.
· Smart Data Acquisition & LPWAN Integration: AI-enhanced telemetry using LPWAN (LoRaWAN, NB-IoT), edge computing for electrical data processing, and remote condition monitoring.
· Renewable Energy Systems: AI models for solar/wind power generation forecasting, grid synchronization, and energy storage system management (ESS).
· Grid Optimization & Power Quality: Machine learning applications in smart grids, fault diagnosis, dynamic load dispatch, and voltage regulation.
· Predictive Maintenance: Anomaly detection and asset life prediction in power transformers, high-voltage equipment, and distribution networks.
Keywords
electric mobility & smart charging, smart data acquisition & LPWAN integration, renewable energy systems, grid optimization & power quality, predictive maintenance
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