Bhargav Appasani – författare

Artificial Intelligence of Things for Wind Energy Systems covers the novel concept of Artificial Intelligence (AI) combined with Internet of Things (IoT), for efficient data acquisition, analytics, and automated control, in wind energy systems. The book begins by introducing Artificial Intelligence of Things (AIoT), technologies, challenges, and future directions. Subsequent chapters provide in-depth coverage of specific technical areas and their applications in wind forecasting, generation, energy management, anomaly detection, digital twins, inspection of generators, recommendation systems, security concerns, and microgrid monitoring and control. Throughout the book, the information presented is supported by visuals, case studies, simulations, and code. A Volume in the Elsevier Wind Energy Engineering Series, this is a valuable resource for researchers, graduate students, engineers, operators, and other industry personnel with an interest in the use of AI and IoT in wind energy production and integration.Links Artificial Intelligence and Internet of Things, in the context of wind energy systemsConsiders the latest innovations in terms of big data, machine learning, deep learning, digital twins, and other areasSupported by case studies, visual decision-making tools, simulations, and code

The book covers the emerging communication and computational technologies for future cyber-physical systems and discusses the security of in-vehicle communication protocols using automotive embedded systems, presenting an in-depth analysis across various domains, such as manufacturing, transportation, health-care, and smart cities.This book:Discusses how communication and computing co-design provides dynamic adaptability and centralized control.Presents the convergence of physical and digital realities within the metaverse and multiverse, setting the stage for the future of cyber-physical-social systems (CPSS).Presents emerging communication and computational technologies, such as 6G, software-defined networking, cloud computing, blockchain, artificial intelligence, machine learning, virtual reality, and blockchain, for the design and implementation of cyber-physical systems.Explores advanced topics such as security and privacy in industrial CPS, strategies for protecting serial industrial networks, and enhancing firmware update security in automotive systems.It is primarily written for senior undergraduates, graduate students, and academic researchers in the fields of electrical engineering, electronics and communication engineering, computer science and engineering, and information technology.

Engineering Applications of AI for Demand Forecasting explores how Artificial Intelligence enhances prediction accuracy across modern engineering systems. As industries move toward Industry 4.0, the book highlights the role of AI in processing large, dynamic datasets for smarter decision-making. This book brings together contemporary research that demonstrates AI’s ability to enhance precision, efficiency, and adaptability in diverse forecasting environments. By covering cybersecurity analytics, anomaly detection, logistics forecasting, sustainable supply chain management, and predictive maintenance, it demonstrates AI’s versatility in complex environments. The book also showcases AI applications in renewable energy forecasting, peak load prediction, smart meter analytics, and prosumer-driven demand modeling. Combining theory with practical case studies, it serves as a valuable resource for engineers, researchers, practitioners, and students.

This book is the first on Smart Grid 3.0. The book presents literature reviews of recent computational and communication technologies and their application in the evolution of smart grids to Smart Grid 3.0.

This book is the first on Smart Grid 3.0. The book presents literature reviews of recent computational and communication technologies and their application in the evolution of smart grids to Smart Grid 3.0.

This book introduces readers to the field of explainable artificial intelligence (XAI), which aims to make AI models more transparent and trustworthy. It explores how XAI can enhance trust and confidence in AI models and their decisions across various innovative applications in fields such as healthcare, finance, and engineering, where AI can significantly impact quality of life. Readers will discover emerging trends related to XAI—such as large language models, generative AI, and natural language processing—that are transforming the landscape of AI research and applications. Featuring an interdisciplinary overview, the book examines the state of the art, challenges, and opportunities in XAI, accompanied by clear examples and detailed explanations of its methods and techniques. The book also offers a balanced perspective on the limitations and trade-offs of XAI and outlines future directions and opportunities for both research and practice. This book is intended for anyone who wants to learn more about XAI and understand how it can enhance trust in AI models.

This book discusses about the new techniques of power generation control of oscillating water column (OWC) using airflow control and maximum power point tracking of OWC using rotational speed control.