Next Generation Artificial Intelligence Driven Smart And Renewable Energy
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Next-Generation Artificial Intelligence Driven Smart and Renewable Energy
To provide for a sustainable future, the potential synergies at the dynamic intersection of renewable energy (RE) incorporated with smart energy and artificial intelligence (AI) must be exploited. RE is crucial to preserve the environment. Energy involving various systems must be optimized and assessed to provide better performance. However, the design and development of RE systems remains a challenge. Advanced optimization techniques, AI, and machine learning (ML) plays a crucial role in implementing the latest innovative research in the field of renewable energy-integrated electrical systems. This book also describes the practical challenges encountered, and the solutions and future scope to be adopted. Applications of a variety of advanced optimization and AI techniques in the design and development of RE-integrated systems are discussed to provide new solutions in the RE domain. Key features: Discusses modern modeling/control approaches for improving renewable energy integrating artificial intelligence-driven power systems Describes the principles and methods of renewable energy generation technologies, and an analysis of their implementation, management, and optimization, and related economic advantages Presents critical information on the technological design and policy issues that must be taken into considered while implementing a smart grid Explains of the metaheuristic optimization algorithm for complex electrical systems, and the whale optimization algorithm-based multi-objective hydrothermal scheduling Covers the electric vehicle charging station in the distribution network, and transient stability constraint optimal power flow problem using chaotic quasi-oppositional chemical reaction optimization The topics covered including microgrids, wind power, solar photo voltaic (PV), optimal power flow (OPF), grid connected inverter, electric vehicle, combined heat and power economic dispatch, FACTS tools for smart energy, harmonic impedance of a salient pole synchronous generator (HI), maximum power point tracking (MPPT) and advanced optimization techniques. Next Generation Artificial Intelligence-Driven Smart and Renewable Energy is ideal for academicians, practitioners, teachers, engineers, industry professionals, researchers, and students in diverse fields, including electrical engineering, electronics and communications engineering, energy, and environmental engineering.
Innovations in Next-Generation Energy Storage Solutions
Global energy demand continuously increases due to population growth and economic development. This rise creates a pressing need to explore new materials for energy harvesting and storage. New findings have been found related to synthesis, fabrication, structure, properties, performance, and technological application. Further exploration into these advancements may inform strategies and policies regarding energy harvesting, energy storage materials, and devices. Innovations in Next-Generation Energy Storage Solutions covers recent advances and trends related to the materials for energy harvesting and storage, bringing together researchers from across physics, materials science, engineering, chemistry, and related fields. Covering topics such as solar cells, hybrid energy, and electrochemical processes, this book is an excellent resource for material scientists, engineers, energy activists, professionals, researchers, scholars, academicians, and more.
Artificial Intelligence-Enabled Digital Twin for Smart Manufacturing
Author: Amit Kumar Tyagi
language: en
Publisher: John Wiley & Sons
Release Date: 2024-10-15
An essential book on the applications of AI and digital twin technology in the smart manufacturing sector. In the rapidly evolving landscape of modern manufacturing, the integration of cutting-edge technologies has become imperative for businesses to remain competitive and adaptive. Among these technologies, Artificial Intelligence (AI) stands out as a transformative force, revolutionizing traditional manufacturing processes and making the way for the era of smart manufacturing. At the heart of this technological revolution lies the concept of the Digital Twin—an innovative approach that bridges the physical and digital realms of manufacturing. By creating a virtual representation of physical assets, processes, and systems, organizations can gain unprecedented insights, optimize operations, and enhance decision-making capabilities. This timely book explores the convergence of AI and Digital Twin technologies to empower smart manufacturing initiatives. Through a comprehensive examination of principles, methodologies, and practical applications, it explains the transformative potential of AI-enabled Digital Twins across various facets of the manufacturing lifecycle. From design and prototyping to production and maintenance, AI-enabled Digital Twins offer multifaceted advantages that redefine traditional paradigms. By leveraging AI algorithms for data analysis, predictive modeling, and autonomous optimization, manufacturers can achieve unparalleled levels of efficiency, quality, and agility. This book explains how AI enhances the capabilities of Digital Twins by creating a powerful tool that can optimize production processes, improve product quality, and streamline operations. Note that the Digital Twin in this context is a virtual representation of a physical manufacturing system, including machines, processes, and products. It continuously collects real-time data from sensors and other sources, allowing it to mirror the physical system’s behavior and performance. What sets this Digital Twin apart is the incorporation of AI algorithms and machine learning techniques that enable it to analyze and predict outcomes, recommend improvements, and autonomously make adjustments to enhance manufacturing efficiency. This book outlines essential elements, like real-time monitoring of machines, predictive analytics of machines and data, optimization of the resources, quality control of the product, resource management, decision support (timely or quickly accurate decisions). Moreover, this book elucidates the symbiotic relationship between AI and Digital Twins, highlighting how AI augments the capabilities of Digital Twins by infusing them with intelligence, adaptability, and autonomy. Hence, this book promises to enhance competitiveness, reduce operational costs, and facilitate innovation in the manufacturing industry. By harnessing AI’s capabilities in conjunction with Digital Twins, manufacturers can achieve a more agile and responsive production environment, ultimately driving the evolution of smart factories and Industry 4.0/5.0. Audience This book has a wide audience in computer science, artificial intelligence, and manufacturing engineering, as well as engineers in a variety of industrial manufacturing industries. It will also appeal to economists and policymakers working on the circular economy, clean tech investors, industrial decision-makers, and environmental professionals.