Industry 4.0-Driven Smart Energy Solutions: Advancements in Renewable Energy Systems

Dear Colleagues,

The rapid global shift towards sustainable energy systems has accelerated advancements across diverse sectors, including solar, wind, hydropower, geothermal, bioenergy, and hybrid energy solutions. To meet the growing demand for efficient, reliable, and intelligent energy systems, there is a need to integrate cutting-edge Industry 4.0 technologies, such as artificial intelligence (AI), machine learning (ML), digital twins (DTs), and the Internet of Things (IoT), into traditional modeling and simulation techniques.

The integration of advanced sensors, real-time monitoring systems, and AI-driven analytics has transformed the way in which renewable energy systems are designed, operated, and optimized. These technologies not only enhance performance, fault detection, and predictive maintenance in energy systems but also enable smart energy management and grid integration, contributing to global sustainability goals.

This Special Issue will present and disseminate the latest innovations, methodologies, and applications in the realm of Industry 4.0-driven smart energy solutions, with a particular focus on sustainable energy systems, intelligent modeling, real-time monitoring, and AI-powered optimization techniques.

Topics of interest include, but are not limited to, the following:

• Renewable Energy Systems:
• Solar energy;
• Wind energy systems;
• Hydroelectric and tidal energy;
• Geothermal and bioenergy solutions;
• Hybrid renewable energy systems.

• Industry 4.0 Applications in Renewable Energy:
• Artificial intelligence (AI), machine learning (ML), and deep learning applications;
• Digital twins (DTs) for real-time system simulation and optimization;
• IoT and sensor integration for smart energy systems;
• Big data analytics and cloud computing in energy management;
• Cyber–physical systems for energy grid optimization.

• Sensors and Monitoring Technologies:
• Advanced sensor networks for energy systems;
• Real-time monitoring, fault detection, and predictive maintenance;
• Smart metering and data acquisition systems;
• Thermal, vibration, and structural health monitoring.

• Modeling, Simulation, and Optimization:
• Integration of AI-driven tools into traditional modeling techniques;
• Multiphysics and multi-scale simulations in renewable energy;
• Energy storage modeling (thermal, chemical, and mechanical);
• Optimization algorithms for energy system performance.

• Sustainable Energy Management and Applications:
• Smart grids and microgrids;
• Energy storage integration (batteries, thermal energy storage, etc.);
• Energy-efficient building systems and smart cities;
• Electric vehicles and charging infrastructure;
• Distributed energy resource management.

For this Special Issue, we welcome submissions of original research papers, review articles, and case studies that explore the synergy between traditional simulation techniques and modern Industry 4.0 technologies in the field of sustainable energy systems.

Dr. Mohamed Shameer Peer Mohamed
Dr. Tsega Yenew Melesse
Dr. Suganthi Ramasamy
Dr. Vigneselvan Sivasubramaniyam
Guest Editors

Manuscript Submission Information

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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.

• renewable energy systems
• Industry 4.0 technologies
• artificial intelligence
• machine learning
• digital twin
• smart sensors and IoT
• modeling and simulation
• real-time monitoring and fault detection
• energy storage and management
• sustainable energy optimization