This book provides readers with emerging research that explores the theoretical and practical aspects of implementing new and innovative artificial intelligence (AI) techniques for renewable energy systems. The contributions offer broad coverage on economic and promotion policies of renewable energy and energy-efficiency technologies, the emerging fields of neuro-computational models and simulations under uncertainty (such as fuzzy-based computational models and fuzzy trace theory), evolutionary computation, metaheuristics, machine learning applications, advanced optimization, and stochastic models. This book is a pivotal reference for IT specialists, industry professionals, managers, executives, researchers, scientists, and engineers seeking current research in emerging perspectives in artificial intelligence, renewable energy systems, and energy autonomy.
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 systems - Builds knowledge and tools in a systematic manner, from fundamentals to advanced applications - Includes dedicated chapters with case studies and applications for each sustainable energy source
Introducing a framework for obtaining and maintaining renewable energy security at the local community level Local energy communities are a framework for assembling and coordinating major stakeholders, individual, corporate, and institutional, in the pursuit of long-term renewable energy projects in a given area. They are aimed at community benefits rather than profit, and have become an invaluable tool in the fight to reimagine the global power grid, one community at a time. With climate change making this fight ever more urgent, integrated local energy communities (ILECs) have never been a more important social force. Integrated Local Energy Communities offers a framework for designing, planning, and operating one of these communities from end to end. Incorporating regulatory and policy issues, the mechanics of local multi-carrier energy systems, and more, it provides viable solutions to one of the most urgent energy challenges of our time. The result is an indispensable contribution to a potentially transformative process. Integrated Local Energy Communities readers will also find: Comprehensive coverage of all types of energy conversion Analysis of the entire value chain, from concepts to planning to operation Discussion of all key actors for integrating the ILEC energy paradigm at the local level Integrated Local Energy Communities is ideal for power engineers, electrical engineers, engineering scientists working in consultancy and industry, as well as the libraries that serve them.
Convergence Strategies for Green Computing and Sustainable Development presents a comprehensive exploration of the potential of emerging technologies, such as the Internet of Things (IoT), Artificial Intelligence (AI), fog computing, and cloud computing, to aid in fostering a sustainable future. It examines how these technologies can reduce the impact of unsustainability in societies, the environment, and natural resources, offering invaluable insights into harnessing their power for positive change. Convergence Strategies for Green Computing and Sustainable Development serves as a comprehensive strategy that holistically understands, transforms, and develops technological systems in society. This book caters to a diverse range of readers, including graduate students, researchers, working professionals seeking knowledge, and industry experts seeking information about new trends. With its recommended topics and comprehensive table of contents, readers can gain in-depth knowledge about sustainable cloud computing, artificial intelligence and machine learning for sustainable development, sustainable wireless systems and networks, and the crucial role of green IoT and Edge-AI in driving a sustainable digital transition.
Advances of Artificial Intelligence in a Green Energy Environment reviews the new technologies in intelligent computing and AI that are reducing the dimension of data coverage worldwide. This handbook describes intelligent optimization algorithms that can be applied in various branches of energy engineering where uncertainty is a major concern. Including AI methodologies and applying advanced evolutionary algorithms to real-world application problems for everyday life applications, this book considers distributed energy systems, hybrid renewable energy systems using AI methods, and new opportunities in blockchain technology in smart energy. Covering state-of-the-art developments in a fast-moving technology, this reference is useful for engineering students and researchers interested and working in the AI industry. - Looks at new techniques in artificial intelligence (AI) reducing the dimension of data coverage worldwide - Chapters include AI methodologies using enhanced hybrid swarm-based optimization algorithms - Includes flowchart diagrams for exampling optimizing techniques
As energy industries produce ever more data, firms are harnessing greater computing power, advances in data science, and increased digital connectivity to exploit that data. These trends have the potential to transform the way energy is produced, transported, and consumed.
This book introduces research presented at the “International Conference on Artificial Intelligence: Advances and Applications-2019 (ICAIAA 2019),” a two-day conference and workshop bringing together leading academicians, researchers as well as students to share their experiences and findings on all aspects of engineering applications of artificial intelligence. The book covers research in the areas of artificial intelligence, machine learning, and deep learning applications in health care, agriculture, business and security. It also includes research in core concepts of computer networks, intelligent system design and deployment, real-time systems, WSN, sensors and sensor nodes, SDN and NFV. As such it is a valuable resource for students, academics and practitioners in industry working on AI applications.
This book addresses emerging issues concerning the integration of artificial intelligence systems in our daily lives. It focuses on the cognitive, visual, social and analytical aspects of computing and intelligent technologies, and highlights ways to improve the acceptance, effectiveness, and efficiency of said technologies. Topics such as responsibility, integration and training are discussed throughout. The book also reports on the latest advances in systems engineering, with a focus on societal challenges and next-generation systems and applications for meeting them. Based on the AHFE 2020 Virtual Conference on Software and Systems Engineering, and the AHFE 2020 Virtual Conference on Artificial Intelligence and Social Computing, held on July 16–20, 2020, it provides readers with extensive information on current research and future challenges in these fields, together with practical insights into the development of innovative services for various purposes.
This book explores how Artificial Intelligence (AI), by leading to an increase in the autonomy of machines and robots, is offering opportunities for an expanded but uncertain impact on society by humans, machines, and robots. To help readers better understand the relationships between AI, autonomy, humans and machines that will help society reduce human errors in the use of advanced technologies (e.g., airplanes, trains, cars), this edited volume presents a wide selection of the underlying theories, computational models, experimental methods, and field applications. While other literature deals with these topics individually, this book unifies the fields of autonomy and AI, framing them in the broader context of effective integration for human-autonomous machine and robotic systems. The contributions, written by world-class researchers and scientists, elaborate on key research topics at the heart of effective human-machine-robot-systems integration. These topics include, for example, computational support for intelligence analyses; the challenge of verifying today’s and future autonomous systems; comparisons between today’s machines and autism; implications of human information interaction on artificial intelligence and errors; systems that reason; the autonomy of machines, robots, buildings; and hybrid teams, where hybrid reflects arbitrary combinations of humans, machines and robots. The contributors span the field of autonomous systems research, ranging from industry and academia to government. Given the broad diversity of the research in this book, the editors strove to thoroughly examine the challenges and trends of systems that implement and exhibit AI; the social implications of present and future systems made autonomous with AI; systems with AI seeking to develop trusted relationships among humans, machines, and robots; and the effective human systems integration that must result for trust in these new systems and their applications to increase and to be sustained.
Optimization Techniques for Hybrid Power Systems: Renewable Energy, Electric Vehicles, and Smart Grid is a comprehensive guide that delves into the intricate world of renewable energy integration and its impact on electrical systems. With the current global energy crisis and the urgent need to address climate change, this book explores the latest advancements and research surrounding optimization techniques in the realm of renewable energy. This book has a focus on nature-inspired and meta-heuristic optimization methods, and it demonstrates how these techniques have revolutionized renewable energy problem-solving and their application in real-world scenarios. It examines the challenges and opportunities in achieving a larger utilization of renewable energy sources to reduce carbon emissions and air pollutants while meeting renewable portfolio standards and enhancing energy efficiency. This book serves as a valuable resource for researchers, academicians, industry delegates, scientists, and final-year master's degree students. It covers a wide range of topics, including novel power generation technology, advanced energy conversion systems, low-carbon technology in power generation and smart grids, AI-based control strategies, data analytics, electrified transportation infrastructure, and grid-interactive building infrastructure.
