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This volume aims to provide a state-of-the-art and the latest advancements in the field of intelligent control and smart energy management. Techniques, combined with technological advances, have enabled the deployment of new operating systems in many engineering applications, especially in the domain of transport and renewable resources. The control and energy management of transportation and renewable resources are shifting towards autonomous reasoning, learning, planning and operating. As a result, these techniques, also referred to as autonomous control and energy management, will become practically ubiquitous soon. The discussions include methods, based on neural control (and others) as well as distributed and intelligent optimization. While the theoretical concepts are detailed and explained, the techniques presented are tailored to transport and renewable resources applications, such as smart grids and automated vehicles. The reader will grasp the most important theoretical concepts as well as to fathom the challenges and needs related to timely practical applications. Additional content includes research perspectives and future direction as well as insight into the devising of techniques that will meet tomorrow’s scientific needs. This contributed volume is for researchers, graduate students, engineers and practitioners in the domains of control, energy, and transportation.
This book provides a relatively whole view of data-driven decision-making methods for energy service innovation and energy system optimization. Through personalized energy services provision and energy efficiency improvement, the book can contribute to the green transformation of energy system and the sustainable development of the society. The book gives a new way to achieve smart energy management, based on various data mining and machine learning methods, including fuzzy clustering, shape-based clustering, ensemble clustering, deep learning, and reinforcement learning. The applications of these data-driven methods in improving energy efficiency and supporting energy service innovation are presented. Moreover, this book also investigates the role of blockchain in supporting peer-to-peer (P2P) electricity trading innovation, thus supporting smart energy management. The general scope of this book mainly includes load clustering, load forecasting, price-based demand response, incentive-based demand response, and energy blockchain-based electricity trading. The intended readership of the book includes researchers and engineers in related areas, graduate and undergraduate students in university, and some other general interested audience. The important features of the book are: (1) it introduces various data-driven methods for achieving different smart energy management tasks; (2) it investigates the role of data-driven methods in supporting various energy service innovation; and (3) it explores energy blockchain in P2P electricity trading, and thus supporting smart energy management.
This book introduces the issues and problems that arise when implementing smart energy management for sustainable manufacturing in the automotive manufacturing industry and the analytical tools and applications to deal with them. It uses a number of illustrative examples to explain energy management in automotive manufacturing, which involves most types of manufacturing technology and various levels of energy consumption. It demonstrates how analytical tools can help improve energy management processes, including forecasting, consumption, and performance analysis, emerging new technology identification as well as investment decisions for establishing smart energy consumption practices. It also details practical energy management systems, making it a valuable resource for professionals involved in real energy management processes, and allowing readers to implement the procedures and applications presented.
This book is a contribution from the authors, to share solutions for a better and sustainable power grid. Renewable energy, smart grid security and smart energy management are the main topics discussed in this book.
The focus of this book is smart energy management with the recurring theme being the use of computational and data-driven methods that use requirements/measurement/monitoring data to drive actuation/control, optimization, and resource management. The computational perspective is applied to manage energy, with an emphasis on smart buildings and the smart electric grids.The book also presents computational thinking and techniques such as inferencing and learning for energy management. To this end, this book is designed to help understand the recent research trends in energy management, focusing specifically on the efforts to increase energy efficiency of buildings, campuses, and cities.
This book examines the energy dimension of the smart city from the perspective of urban planning, providing a complete overview that ranges from theoretical aspects to practical considerations and projects. In addition, it aims to illustrate how the concept of the smart city can enhance understanding of the urban system and foster new forms of management of the metropolis, including with respect to energy supply and use. Specifically, the book explores the different dimensions of the relationship between energy and the city, discusses methodological issues with a special focus on ontological approaches to sustainability, and describes practices, tools, and good examples of energy-related urban planning. The authors represent the main Italian research groups working in the field, Italy being an excellent example of a country exposed to energy problems due to, for example, vulnerability to climate change and lack of primary energy resources. This book will be valuable for students of urban planning, town planners, and researchers interested in understanding the changing nature of the city and the challenges posed by energy issues.
The intersection of artificial intelligence (AI) and digital twin technology presents a problem and an unparalleled opportunity for transformation. Businesses grapple with the need for operational excellence, innovation, and a competitive edge, all while navigating the intricate web of data analytics, decision-making, and real-time monitoring. In response to these challenges, Harnessing AI and Digital Twin Technologies in Businesses emerges as an example of insight and guidance, offering a comprehensive exploration of the complementary connection between AI and digital twin technology. In a world where the convergence of these powerful tools transforms business intelligence, enabling initiative-taking decision-making and dynamic simulations. This book serves as a solution for decision-makers, technologists, and researchers seeking to not only understand but harness the potential of AI-powered digital twins to enhance productivity, creativity, and judgment in their operations.
With the growth of renewable energy sources, microgrids have become a key component in the distribution of power to localized areas while connected to the traditional grid or operating in a disconnected island mode. Based on the extensive real-world experience of the authors, this cutting-edge resource provides a basis for the design, installation, and day-by-day management of microgrids. Professionals find coverage of the critical aspects they need to understand, from the initial planning and the selection of the most appropriate technologies and equipment, to optimal management and real-time control. Moreover, this forward-looking book places emphasis on new architectures of the energy systems of the future. Written in accessible language with practical examples, the book explains advanced topics such as optimization algorithms for energy management systems, control issues for both on-grid and island mode, and microgrid protection. Practitioners are also provided with a complete vision for the deployment of the microgrid in smart cities.