Advances in Intelligent Energy Systems – serie

The Agricultural Energy Internet: Theories, Methods, and Future Prospects provides a pioneering guide to the grid integration and impact of agricultural energy systems for a distributed and sustainable power grid. This book begins with an introduction to fundamental concepts that is followed by a comprehensive safety analysis considering crop physiological characteristics. Subsequent chapters delve into enhancing the synergy of agriculture, energy, and the environment through carbon monitoring, optimal management strategies for rural microgrids, and distributed energy planning.Later chapters cover system design insights, load modeling, control strategies, and AI-based tools. Designed for engineers, students, and researchers, this book collates the state of research and provides detailed case studies and practical applications, making it indispensable for those innovating in sustainable energy systems.Includes a clear and incisive introduction to the nexus of agricultural needs, power grids, and smart energyPresents practical toolkits for a variety of scenarios, with detailed case studies, code files, and transparent data sharingProvides a new release in the Advances in Intelligent Energy Systems series, sharing the latest developments in sustainable power for the future

Computer Vision and Machine Intelligence for Renewable Energy Systems offers a practical, systemic guide to the use of computer vision as an innovative tool to support renewable energy integration.This book equips readers with a variety of essential tools and applications: Part I outlines the fundamentals of computer vision and its unique benefits in renewable energy system models compared to traditional machine intelligence: minimal computing power needs, speed, and accuracy even with partial data. Part II breaks down specific techniques, including those for predictive modeling, performance prediction, market models, and mitigation measures. Part III offers case studies and applications to a wide range of renewable energy sources, and finally the future possibilities of the technology are considered. The very first book in Elsevier’s cutting-edge new series Advances in Intelligent Energy Systems, Computer Vision and Machine Intelligence for Renewable Energy Systems provides engineers and renewable energy researchers with a holistic, clear introduction to this promising strategy for control and reliability in renewable energy grids.Provides a sorely needed primer on the opportunities of computer vision techniques for renewable energy systemsBuilds knowledge and tools in a systematic manner, from fundamentals to advanced applicationsIncludes dedicated chapters with case studies and applications for each sustainable energy source

Green Machine Learning and Big Data for Smart Grids: Practices and Applications is a guidebook to the best practices and potential for green data analytics when generating innovative solutions to renewable energy integration in the power grid. This book begins with a solid foundation in the concept of “green” machine learning and the essential technologies for utilizing data analytics in smart grids. A variety of scenarios are examined closely, demonstrating the opportunities for supporting renewable energy integration using machine learning, from forecasting and stability prediction to smart metering and disturbance tests.Uses for control of physical components including inverters and converters are examined, along with policy implications. Importantly, real-world case studies and chapter objectives are combined to signpost essential information, and to support understanding and implementation.Packages core concepts of green machine learning and smart grids in a clear, understandable wayIncludes real-world, practical applications and case studies for replication and innovative solution developmentIntroduces readers with a range of expertise to best practices and the latest technological advances

Machine Learning and Data Analysis for Energy Efficiency in Buildings: Intelligent Operation, Maintenance, and Optimization of Building Energy Systems introduces data basics, from selecting and evaluating data to the identification and repair of abnormalities. Other sections cover data mining applied to energy forecasting, including long- and short-term predictions, the introduction of occupant-focused behavior analysis, and current methods for supply and demand applications. Case studies are included in each part to assist in evaluation and implementation of these techniques across building energy systems.Working from the fundamentals of big data analysis to a complete method for building energy assessment, flexibility, and management, this book provides students, researchers, and professionals with an essential, cutting-edge resource on this important technology.Builds from data basics to complex solutions and applications for energy efficiency in building systemsIncludes step-by-step methods for data anomaly and fault identification, repair, and maintenanceProvides real-world case studies and applications for immediate use in research and industry

Physics-Aware Machine Learning for Integrated Energy Systems Management, a new release in the Advances in Intelligent Energy Systems series, guides the reader through this state-of-the-art approach to computational methods, from data input and training to application opportunities in integrated energy systems. The book begins by establishing the principles, design, and needs of integrated energy systems in the modern sustainable grid before moving into assessing aspects such as sustainability, energy storage, and physical-economic models. Detailed, step-by-step procedures for utilizing a variety of physics-aware machine learning models are provided, including reinforcement learning, feature learning, and neural networks.Supporting students, researchers, and industry engineers to make renewable-integrated grids a reality, this book is a holistic introduction to an exciting new approach in energy systems management.Outlines the challenges, opportunities, and applications for utilizing physics-aware machine learning to support renewable energy integration to the modern gridCovers a wide variety of techniques, from fundamental principles to security concernsRepresents the latest offering in the cutting-edge series, Advances in Intelligent Energy Systems, which introduces these essential multidisciplinary skills to modern energy engineers

Intelligent Control in Smart Energy Systems provides a clear and practical guide to the implementation of Artificial Intelligent (AI) techniques in modern, sustainable energy systems. Building from essential theory to advanced application processes, this book outlines the essential challenge of control for renewable and distributed energy sources and applies a range of AI solutions from deep learning, data-driven decision-making, advanced control, and optimization algorithms for smart grids. Case studies and latest results cover control across design, development, and operation of smart energy systems, touching on control in generation, distribution, and supply.Catering to newly interdisciplinary researchers as well as those with a basic understanding of AI technologies, this book supports higher-stage undergraduates, graduate students, researchers, and system engineers in implementing best practice control techniques for a sustainable energy grid.Incorporates a stepped approach, from basic theoretical principles to advanced application examplesIncludes case studies in a variety of scenarios, from a global perspective of experts, for maximum versatilityAdds to the cutting-edge series Advances in Intelligent Energy Systems, which provides the latest research and best practice for utilizing AI to enable sustainable energy systems

Intelligent Energy Systems using the Barnacles Mating Optimizer and Evolutionary Mating Algorithm: Foundations, Methods, and Applications reveals the potential of innovative optimization algorithms to support sustainability in modern energy systems. This book provides a multidisciplinary foundation for the reader, with Part I breaking down fundamentals including the challenges to be addressed in renewable energy systems and detailed methodologies including swarm-, physics-, and human-based algorithms, before introducing the Barnacles Mating Optimizer and Evolutionary Mating Algorithm themselves. Part II drills deeper into examples, case studies, and applications for energy systems, offering comparative analysis with alternative tools, and providing complimentary MATLAB code using the latest Toolbox. A sandbox for readers to learn, skill-build, and develop in, ‘Intelligent Energy Systems using BMO and EMA’ provides an indispensable guide to these cutting-edge AI tools for new and experienced readers.Builds step-by-step from foundational principles to complex applications in sustainable energy systemsIncludes case studies, tools, and complimentary MATLAB code to try out, rework, and apply to new problemsGuides readers through these innovative methods, as part of the ground-breaking Advances in Intelligent Energy Systems

Advanced Energy Management: Digitalization and AI for the Internet of Energy presents the latest advances in the fields of distributed renewable systems, advanced controls, and energy management of nonlinear energy behaviors. The book addresses the problems of energy resilience under extreme climate and extreme events and presents new applications for energy-efficient, low-carbon, and energy-reliable cities.The book explains how energy management is used in multienergy systems for improved power dispatch, fast response, dynamic aging, and techno-economic performance.This book is an invaluable reference for students, researchers, and industry professionals seeking advanced energy management strategies, and will be of interest to anyone involved in carbon neutrality solutions.Presents the latest developments and future trends in advanced energy management following sustainable energy supply-transmission-distribution with energy storageExplains the fundamentals of how AI can facilitate a low-carbon transition through sustainable energy and storageIncludes case studies and real-world applications of advanced energy management with AI and digitalization

Reliable Non-Parametric Techniques for Energy System Operation and Control: Fundamentals and Applications of Constraint Learning and Safe Reinforcement Learning Methods, a new Volume in the Advances in Intelligent Energy Systems, is a comprehensive guide to modern smart methods in energy system operation and control. This book covers fundamental concepts and applications in both deterministic and uncertain environments. It addresses the challenge of accuracy in imbalanced datasets and the limitations of measurements. The book delves into advanced topics such as safe reinforcement learning for energy system control, including training-efficient intrinsic-motivated reinforcement learning, and physical layer-based control, and more. Other chapters cover barrier function-based control and CVaR-based control for systems without hard operation constraints. Designed for graduate students, researchers, and engineers, this book stands out for its practical approach to advanced methods in energy system control, enabling sustainable developments in real-world conditions.Bridges the gap between theory and practice, providing essential insights for graduate students, researchers, and engineersIncludes visual elements, data and code, and case studies for easy understanding and implementationProvides the latest release in the Advances in Intelligent Energy Systems series, bringing together the latest innovations in smart, sustainable energy

Artificial Intelligence for Energy Efficiency is a comprehensive exploration of AI application technologies. The book integrates AI algorithms with the operational principles of electrical machinery and energy systems and introduces how these algorithms can alter the operational characteristics of such systems. With a focus on their use in enhancing energy efficiency, it imparts interdisciplinary knowledge and a deep understanding of the principles and methods of AI applications. The book begins with an overview of global trends in enhancing energy efficiency, followed by an introduction to AI algorithms applied in this field. Subsequent chapters, supported by industry case studies, delve into AI applications in enhancing motor efficiency, building and energy saving for building equipment, and smart city development. The book also addresses AI computing power saving and discusses the evolution of AI and the limits of energy efficiency improvement. It concludes with future prospects and the most beneficial application of AI for sustainable human development. Artificial Intelligence for Energy Efficiency is an essential read for graduate students in mechanical, electrical, architectural, and energy-related fields, as well as researchers interested in the intersection of AI and energy efficiency. This comprehensive guide offers a wealth of research case studies, making it a valuable reference tool for professionals in the energy sector.Explores the integration of AI algorithms with the operational principles of electrical machinery and energy systems, offering an in-depth understanding of how AI can enhance energy efficiencyAnalyses real-world AI application case studies in factories, buildings, and cities, providing practical insights into the effectiveness of AI applications in improving energy efficiencyEvaluates the energy consumption of AI computations, proposing solutions to perform more computations with relatively lower energy consumptionIncludes reference designs for AI-driven energy-efficient motors, HVAC systems, smart buildings, and sustainable cities