About this Research Topic

Background

The landscape of global energy systems is undergoing a profound transformation, driven by rapid advancements in technology. At the forefront of this evolution are the concepts of 'smart grids', 'smart homes', and 'resilient grids'. A suite of innovative elements including advanced sensor technologies, integration of renewable energy sources, proliferation of Internet of Things (IoT) devices, and the rise of smart appliances characterizes these modern energy systems.

These technological advancements are collectively reshaping how energy systems operate, making them more adaptive, efficient, and resilient. A critical aspect of these modernized grids is the extensive use of sensors and big data, coupled with intelligent control systems and distributed decision-making frameworks. This technological integration aims to create an energy infrastructure that can maintain its core functions despite various internal and external perturbations, ranging from natural disasters to cyber-attacks.

In tandem with these technological advancements, there is an escalating dependence on Artificial Intelligence (AI) and Machine Learning/Deep Learning (ML/DL) techniques among researchers, businesses, and policymakers for enhancing decision-making processes in energy systems. The evolving realm of AI, distinguished by breakthroughs in autonomous systems, advanced predictive analytics, and intricate ML/DL algorithms capable of deciphering complex patterns from sensor data, is poised to revolutionize the energy sector significantly. These AI and ML/DL services are not merely augmenting existing capabilities; they are also forging pathways for ground-breaking applications and solutions.

However, the journey of designing, developing, and implementing such AI and ML/DL-driven services in the energy sector is riddled with unique methodological and technological challenges. These challenges encompass a broad spectrum, including concerns over data privacy, the intricacies of system integration, ensuring reliability, and addressing ethical considerations.

The purpose of this Research Topic is to assemble a diverse range of innovative research and pragmatic solutions that employ AI and ML/DL techniques within energy systems. We are seeking contributions that vividly demonstrate the current and potential future roles of AI and ML/DL in enhancing energy systems. This Topic is envisioned to be an essential resource for comprehending the present status and future trajectories of AI and ML/DL in the context of smart grids.

We invite submissions from researchers, practitioners, and policymakers that explore the diverse applications, advancements, and challenges of AI and ML in smart grids. This Research Topic is an opportunity to contribute to a crucial discussion on the future of energy systems in an increasingly digital and interconnected world.

Topics of interest include:

• Innovations in AI and ML for smart grid optimization.

• AI-driven energy demand forecasting and management.

• ML applications in renewable energy integration.

• AI solutions for smart grid security and resilience.

• Case studies on AI/ML applications in enhancing grid stability and efficiency.

• Comparative studies on traditional vs AI/ML-based smart grid systems.

• Ethical and privacy concerns in AI/ML applications in smart grids - challenges in integrating AI/ML into smart grid systems, including data security and system interoperability.

• Prospects for future advancements in AI and ML within smart grid technologies.

Article types and fees

This Research Topic accepts the following article types, unless otherwise specified in the Research Topic description:

• Brief Research Report
• Clinical Trial
• Community Case Study
• Conceptual Analysis
• Data Report
• Editorial
• General Commentary
• Hypothesis and Theory
• Methods
• ... View all formats

Articles that are accepted for publication by our external editors following rigorous peer review incur a publishing fee charged to Authors, institutions, or funders.

Article types

This Research Topic accepts the following article types, unless otherwise specified in the Research Topic description:

• Brief Research Report
• Clinical Trial
• Community Case Study
• Conceptual Analysis
• Data Report
• Editorial
• General Commentary
• Hypothesis and Theory
• Methods
• Mini Review
• Opinion
• Original Research
• Perspective
• Policy and Practice Reviews
• Review
• Study Protocol
• Systematic Review
• Technology and Code

Keywords: Electrical data, Data analysis, Artificial Intelligence, Machine Learning, Smart Grid

Important note: All contributions to this Research Topic must be 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 suitable section or journal at any stage of peer review.

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