The landscape of global energy systems is undergoing profound transformation, driven by rapid technological progress. At the forefront of this development are the concepts of “smart grids”, “smart homes” and “resilient networks”. A suite of innovative elements, including advanced sensor technologies, the integration of renewable energy sources, the proliferation of Internet of Things (IoT) devices, and the rise of smart appliances, characterize these modern energy systems.
These technological advancements are collectively reshaping the way energy systems operate, making them more adaptive, efficient and resilient. A key aspect of these modernized networks is the extensive use of sensors and big data, combined with intelligent control systems and distributed decision-making frameworks. This technological integration aims to create an energy infrastructure capable of maintaining its essential functions despite various internal and external disruptions, ranging from natural disasters to cyberattacks.
Alongside these technological advancements, researchers, businesses, and policymakers are increasingly relying on artificial intelligence (AI) and machine learning/deep learning (ML/DL) techniques to improve decision-making processes in systems. energy. The evolving field of AI, characterized by breakthroughs in autonomous systems, advanced predictive analytics, and complex ML/DL algorithms capable of deciphering complex patterns from sensor data, is poised for a major revolution. significantly in the energy sector. These AI and ML/DL services don’t simply augment existing capabilities; they also pave the way for revolutionary applications and solutions.
However, the journey of designing, developing and implementing such AI and ML/DL-based services in the energy sector is fraught with unique methodological and technological challenges. These challenges cover a broad spectrum, including concerns about data privacy, the intricacies of systems integration, ensuring reliability, and addressing ethical considerations.
The aim of this research topic is to bring together a diverse range of innovative research and pragmatic solutions that utilize AI and ML/DL techniques within energy systems. We are looking for contributions that vividly demonstrate the current and potential future roles of AI and ML/DL in improving energy systems. This topic is considered an essential resource for understanding the current state and future trajectories of AI and ML/DL in the context of smart grids.
We invite researchers, practitioners and policymakers to submit proposals that explore the diverse applications, advances and challenges of AI and ML in smart grids. This research topic is an opportunity to contribute to a crucial discussion about the future of energy systems in an increasingly digital and interconnected world.
Topics of interest include:
• Innovations in AI and ML for the optimization of smart grids.
• AI-based energy demand forecasting and management.
• ML applications in the integration of renewable energies.
• AI solutions for smart grid security and resilience.
• Case studies on AI/ML applications to improve network stability and efficiency.
• Comparative studies on traditional and AI/ML-based smart grid systems.
• Ethics and Privacy Issues in AI/ML Applications in Smart Grids: Challenges in integrating AI/ML into smart grid systems, including data security and interoperability systems.
• Prospects for future advancements in AI and ML in smart grid technologies.
Item Types and Fees
This research topic accepts the following article types unless otherwise noted in the research topic description:
- Brief research report
- Clinical trial
- Community Case Study
- Conceptual analysis
- Data report
- Editorial
- General comment
- Hypothesis and theory
- Methods
Articles accepted for publication by our external editors after rigorous peer review incur a publication fee charged to the authors, institutions or funders.
Keywords : Electrical data, Data analysis, Artificial Intelligence, Machine Learning, Smart Grid
Important note: All contributions to this research topic must fall within the scope of the section and journal to which they are submitted, as defined in their mission statements. Frontiers reserves the right to guide an out-of-scope manuscript to a more appropriate section or journal at any stage of peer review.
