The energy industry worldwide is facing one of the most profound changes in its history, which will be accompanied by breakthrough innovations and the exponentially evolving use of artificial intelligence in business processes. In addition to the use of artificial intelligence and AI-supported unmanned systems (on land, at sea and in the air), distributed-ledger-technologies, extended reality and 3D-print based on cyber-physical systems and the Internet of Things, as well as process mining, robotic process automation, data science and cloud computing, for example, will not only decisively shape a sustainable energy supply system in the future, but also accelerate the transformation to energy industry 4.0. At the same time, the increasingly strong networking (smart grid, smart meter, smart home, smart city) of the energy industry and its environment is associated with a growing risk potential, which must be expanded in the future as part of a high-quality cyber resilience, in particular through the use of artificial intelligence. Without the development and use of innovations and artificial intelligence in the context of increasingly digitized business processes, there is a risk that neither the energy transition can be successfully implemented nor climate change combated. In addition to the fundamentals of the classic, primarily analog energy industry, the publication addresses the possible paradigm shift that will be characterized by innovations, disruptive technologies and digital business models in the energy industry.

This book looks at industry change patterns and innovations (such as artificial intelligence, machine learning, big data analysis, and blockchain support and efficiency technology) that are speeding up industrial transformation, industrial infrastructure, biodiversity, and productivity. This book focuses on real-world industrial applications and case studies to provide for a wider knowledge of intelligent manufacturing. It also offers insights into manufacturing, logistics, and supply chain, where systems have undergone an industrial transformation. It discusses current research of machine learning along with blockchain techniques that can fill the gap between research and industrial exposure. It goes on to cover the effects that the Fourth Industrial Revolution has on industrial infrastructures and looks at the current industry change patterns and innovations that are accelerating industrial transformation activities. Researchers, scholars, and students from different countries will appreciate this book for its real-world applications and knowledge acquisition. This book targets manufacturers, industry owners, product developers, scientists, logistics, and supply chain engineers. Focuses on real-world industrial applications and case studies to provide for a wider knowledge of intelligent manufacturing Offers insights into manufacturing, logistics, and supply chain where systems have undergone an industrial transformation Discusses current research of machine learning along with blockchain techniques that can fill the gap between research and industrial exposure Covers the effects that the 4th Industrial Revolution has on industrial infrastructures Looks at industry change patterns and innovations that are speeding up industrial transformation activities

This book focuses on how businesses manage organizational innovation processes. It explores the innovative policies and practices that organizations need to develop to allow them to be successful in this digital age. These policies will be based on key resources such as research and development and human resources and need to enable companies to respond to challenges they may face due to the digital economy. It explains how organizational innovation can be used to improve business’s development, performance, conduct and outcomes. Contributing to stimulate the growth and development of each individual in a dynamic, competitive and global economy, the present book can be used by a diverse range of readers, including academics, researchers, managers and engineers interested in matters related with Organizational Innovation in the Digital Age.

Artificial intelligence (AI) is now mediating, and in some cases seen to be controlling, key urban services and infrastructures, thus becoming a prominent feature of the contemporary city. As portrayed in recent studies, the “autonomous city” can be understood as a city where urban artificial intelligences perform tasks and take on roles which have traditionally been the domain of humans. At stake in these debates are questions related to the meaning and ongoing role of intelligence, for both humans and machines. While autonomous cars transport people, service robots run shops, drones deliver goods and city brains govern entire cities, humans are redefining the meaning of what “smart” means in the city and what role the human being may play in future urban spaces. With humans shifted to new sectors of the economy or pushed aside by algorithms and robotic agents creating new ways of seeing and governing the city, we raise the question as to whether or not cities are becoming more autonomous from human experience in the sense that their operation does not rely as much on human inputs anymore.

Forward thinking resource discussing emerging AI and IoT technologies and how they are applied to Industry 4.0 Topics in Artificial Intelligence Applied to Industry 4.0 discusses the design principles, technologies, and applications of emerging AI and IoT solutions on Industry 4.0, explaining how to make improvements in infrastructure through emerging technologies. Providing a clear connection with different technologies such as IoT, Big Data, AR and VR and Blockchain, this book presents security, privacy, trust, and other issues whilst delving into real-world problems and case studies. The text takes a highly practical approach, with a clear insight on how readers can increase productivity by drastically shortening the time period between the development of a new product and its delivery to customers in the market by 50%. This book also discusses how to save energy across systems to ensure competitiveness in a global market, and become more responsive in how they produce products and services for their consumers, such as by investing in flexible production lines. Written by highly qualified authors, Topics in Artificial Intelligence Applied to Industry 4.0 explores sample topics such as: Quantum machine learning, neural network implementation, and cloud and data analytics for effective analysis of industrial data Computer vision, emerging networking technologies, industrial data spaces, and an industry vision for 2030 in both developing and developed nations Novel or improved nature-inspired optimization algorithms in enhancing Industry 5.0 and the connectivity of any components for smart environment Future professions in agriculture, medicine, education, fitness, R&D, and transport and communication as a result of new technologies Aimed at researchers and students in the interdisciplinary fields of Smart Manufacturing and Smart Applications, Topics in Artificial Intelligence Applied to Industry 4.0 provides the perfect overview of technology from the perspective of modern society and operational environment.

Smart grid (SG) is considered a form of intelligent system that allows the electric grid to perform its functions efficiently. The SG is a network that allows for the flow of electrical energy and data, where the data is used to make intelligent decisions in the operation of the electric grid. Artificial intelligence (AI) techniques, such as expert system (ES), Machine Learning (ML), and deep Learning (DL) have brought an advancing frontier in power electronics and power engineering with their powerful data processing capabilities. The SG relies on the flow of data to make its intelligent control; therefore, AI technology is a perfect fit for the SG. The application of AI technology in the SG has the potential to improve the intelligence of the SG. This research topic is focused on ways of improving the data analysis and control of SG by leveraging technologies. Manuscripts with the progress made in solving a range of miscellaneous and critical problems in SG by leveraging AI methods such as ES, ML, and DL methods are welcome. Reviews and original research that describe the latest developments in this field are considered for publication in this research topic. The scope of this Research Topic will include the following themes, but are not limited to: 1. Data-driven and artificial intelligence approaches to enhancing flexibility and resilience of SG. 2. Expert system, Machine Learning and Deep Learning, reinforcement learning and transfer learning for applications in SG. 3. AI for development in ensuring high reliability and stability of electric power system with high penetration of renewable energy. 4. AI for studies in operation protection, integrated planning, and control of SG systems. 5. AI for development in diagnostics and diagnostics for SG. 6. Health monitoring of a modern wind generation system using an adaptive neuro-fuzzy system. 7. Space vector fault pattern identification of a smart grid subsystem by neural mapping. 8. Control techniques, mathematical programming methods, optimization techniques and metaheuristics applied in SG. 9. AI and optimization techniques for green energy and carbon footprint. 10. Novel applications of AI-based smart grids in smart cities, smart transportation, smart healthcare, and smart manufacturing.