Artificial Intelligence (AI) and Internet of Things (IoT) Applications for Resilient and Sustainable Energy Systems

Dear Colleagues,

Artificial Intelligence (AI) and the Internet of Things (IoT) have the potential to significantly contribute to achieving a more environmentally friendly and resilient energy future. AI technologies have the capability to address environmental goals, enhance energy security, and optimize energy generation to decrease carbon emissions. Furthermore, machine learning models can improve the reliability of the power grid by forecasting severe weather conditions and their effects on infrastructure. IoT has multiple applications in power systems, such as smart grid automation, advanced metering infrastructure (AMI), grid asset maintenance, remote infrastructure maintenance, demand response and load balancing, distributed energy resource (DER) management, workplace and employee safety, smart metering and billing, and connected public lighting. The IoT can be employed to efficiently oversee and control public lighting installations. By harnessing the power of AI and IoT technologies, we can efficiently tackle environmental goals, bolster energy security, and optimize energy production to reduce carbon emissions.

Scope and Purpose:

This Special Issue on “Artificial Intelligence (AI) and Internet of Things (IoT) Applications for Resilient and Sustainable Energy Systems” aims to explore the cutting-edge integration of AI and IoT technologies in the energy sector. The focus is on enhancing the resilience and sustainability of energy systems through innovative applications. This issue will delve into the latest research, case studies, and technological advancements that demonstrate how AI and IoT can optimize energy management, predict and prevent system failures, integrate renewable energy sources, and facilitate smart grid operations. The convergence of AI and IoT in energy systems is a burgeoning field that builds upon extensive research in both domains. Previous literature has extensively explored AI techniques for data analysis, predictive modeling, and optimization, while IoT research has focused on the deployment of interconnected devices for real-time monitoring and control. However, the specific application of these technologies to create resilient and sustainable energy systems remains an emerging area. This Special Issue seeks to bridge this gap by providing comprehensive insights and practical examples of AI and IoT integration in energy systems.

Supplementing Existing Literature:

This Special Issue will significantly supplement existing literature in the following ways:

• Providing detailed case studies that illustrate successful implementations of AI and IoT in energy management.
• Introducing novel AI algorithms and IoT architectures designed specifically for energy applications.
• Addressing the challenges and solutions related to the integration of AI and IoT in the energy sector, including scalability, security, and data privacy.
• Highlighting interdisciplinary approaches that combine AI, IoT, and energy system engineering to achieve sustainable outcomes.
• Offering policy and regulatory perspectives on the deployment of AI and IoT in energy systems.

Research Areas:

The Special Issue will cover a wide range of research areas. Original research articles and reviews are welcome. Research areas may include, but are not limited to, the following:

• AI Algorithms for Energy Optimization;
• IoT Architectures for Smart Grids;
• Predictive Maintenance Using AI and IoT;
• Real-Time Energy Monitoring and Control;
• Demand Response and Load Forecasting;
• Integration of Renewable Energy Sources;
• Cybersecurity in AI and IoT-Enabled Energy Systems;
• AI for Environmental Impact Assessment;
• Smart Building Energy Management;
• Distributed Energy Resource Management;
• Policy and Regulatory Implications;
• Case Studies on AI and IoT Applications in Energy Systems.

This Special Issue will provide a comprehensive overview of the current state of AI and IoT applications in energy systems, highlight the potential for future advancements, and serve as a valuable resource for researchers, practitioners, and policymakers in the field.

Dr. Tarek Kandil
Dr. Hassan M. Hussein Farh
Prof. Dr. Saad Mekhilef
Prof. Dr. Hayrettin Karayaka
Dr. Adam Harris
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. AI is an international peer-reviewed open access quarterly 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 1600 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.

• artificial intelligence (AI)
• internet of things (IoT)
• machine learning
• renewable energy integration
• smart grid technologies
• grid resilience
• cybersecurity in energy systems
• distributed energy resources
• load forecasting
• energy management and efficiency