Artificial intelligence techniques applied in the power system sector make the prediction of renewable power source generation and demand more efficient and effective. Additionally, since renewable sources are intermittent in nature, it is necessary to predict and analyze the data of input sources. Hence, further study on the prediction and data analysis of renewable energy sources for sustainable development is required. AI Techniques for Renewable Source Integration and Battery Charging Methods in Electric Vehicle Applications focuses on artificial intelligence techniques for the evolving power system field, electric vehicle market, energy storage elements, and renewable energy source integration as distributed generators. Covering key topics such as deep learning, artificial intelligence, and smart solar energy, this premier reference source is ideal for environmentalists, computer scientists, industry professionals, researchers, academicians, scholars, practitioners, instructors, and students.

This book presents the latest research on applications of artificial intelligence and the Internet of Things in renewable energy systems. Advanced renewable energy systems must necessarily involve the latest technology like artificial intelligence and Internet of Things to develop low cost, smart and efficient solutions. Intelligence allows the system to optimize the power, thereby making it a power efficient system; whereas, Internet of Things makes the system independent of wire and flexibility in operation. As a result, intelligent and IOT paradigms are finding increasing applications in the study of renewable energy systems. This book presents advanced applications of artificial intelligence and the internet of things in renewable energy systems development. It covers such topics as solar energy systems, electric vehicles etc. In all these areas applications of artificial intelligence methods such as artificial neural networks, genetic algorithms, fuzzy logic and a combination of the above, called hybrid systems, are included. The book is intended for a wide audience ranging from the undergraduate level up to the research academic and industrial communities engaged in the study and performance prediction of renewable energy systems.

In today’s modern society, to reduce the carbon dioxide gas emission from motor vehicles and to save mother nature, electric vehicles are becoming more practical. As more people begin to see the benefits of this technology, further study on the challenges and best practices is required. Artificial Intelligence Applications in Battery Management Systems and Routing Problems in Electric Vehicles focuses on the integration of renewable energy sources with the existing grid, introduces a power exchange scenario in the prevailing power market, considers the use of the electric vehicle market for creating cleaner and transformative energy, and optimizes the control variables with artificial intelligence techniques. Covering key topics such as artificial intelligence, smart grids, and sustainable development, this premier reference source is ideal for government officials, industry professionals, policymakers, researchers, scholars, practitioners, academicians, instructors, and students.

INTELLIGENT RENEWABLE ENERGY SYSTEMS This collection of papers on artificial intelligence and other methods for improving renewable energy systems, written by industry experts, is a reflection of the state of the art, a must-have for engineers, maintenance personnel, students, and anyone else wanting to stay abreast with current energy systems concepts and technology. Renewable energy is one of the most important subjects being studied, researched, and advanced in today’s world. From a macro level, like the stabilization of the entire world’s economy, to the micro level, like how you are going to heat or cool your home tonight, energy, specifically renewable energy, is on the forefront of the discussion. This book illustrates modelling, simulation, design and control of renewable energy systems employed with recent artificial intelligence (AI) and optimization techniques for performance enhancement. Current renewable energy sources have less power conversion efficiency because of its intermittent and fluctuating behavior. Therefore, in this regard, the recent AI and optimization techniques are able to deal with data ambiguity, noise, imprecision, and nonlinear behavior of renewable energy sources more efficiently compared to classical soft computing techniques. This book provides an extensive analysis of recent state of the art AI and optimization techniques applied to green energy systems. Subsequently, researchers, industry persons, undergraduate and graduate students involved in green energy will greatly benefit from this comprehensive volume, a must-have for any library. Audience Engineers, scientists, managers, researchers, students, and other professionals working in the field of renewable energy.

Today, the global power demand relies on a delicate balance between conventional and renewable energy systems, necessitating both efficient power generation and the effective utilization of these energy resources through appropriate energy storage solutions. Integrating microgrid systems into the utility grid has become a critical facet of modern power systems. The intermittent and unpredictable nature of these energy sources poses a formidable challenge for academic scholars and researchers. This compels them to explore under-investigated areas, including energy source estimation, storage elements, load pattern prediction, coordination among distributed sources, and the development of energy management algorithms for precise and efficient control. AI Approaches to Smart and Sustainable Power Systems tackles these issues using cutting-edge AI techniques. It examines the most effective methods to optimize voltage, frequency, power, fault diagnosis, component health, and overall power system quality and reliability. AI empowers predictive and preventive maintenance for a sustainable energy future. The book focuses on emerging research areas, including renewable energy, power flow calculations, demand scheduling, real-time performance validation, and AI integration into modern power systems, accompanied by insightful case studies.

Infrastructure development presents significant challenges for both developing and developed countries, hindering their progress in achieving the Sustainable Development Goals (SDGs). Governments often struggle to effectively leverage the necessary resources and expertise for financing and managing infrastructure projects, resulting in untapped potential for sustainable and inclusive development. Achieving the Sustainable Development Goals Through Infrastructure Development, edited by Cristina Raluca Gh. Popescu, Poshan Yu, and Yue Wei, offers a comprehensive guide to address these challenges. Focusing on public-private partnerships (PPPs) as a transformative solution, the book equips policymakers, investors, practitioners, and researchers with the essential knowledge and tools needed to navigate the complexities of infrastructure development and leverage the expertise and resources of the private sector. By showcasing successful case studies, analyzing critical success factors, and providing valuable insights into the implementation of PPPs in both developing and developed countries, this book becomes an indispensable resource for driving progress towards the SDGs. Covering crucial topics such as financing, risk management, legal frameworks, and sustainability considerations, it empowers readers to make informed decisions and foster collaborative partnerships between the public and private sectors. Through its comprehensive roadmap, this book enables stakeholders to unlock the full potential of sustainable and inclusive infrastructure development, paving the way for a prosperous future for all.

In recent years, artificial intelligence (AI) has drawn significant attention with respect to its applications in several scientific fields, varying from big data handling to medical diagnosis. A tremendous transformation has taken place with the emerging application of AI. AI can provide a wide range of solutions to address many challenges in civil engineering. Artificial Intelligence and Machine Learning Techniques for Civil Engineering highlights the latest technologies and applications of AI in structural engineering, transportation engineering, geotechnical engineering, and more. It features a collection of innovative research on the methods and implementation of AI and machine learning in multiple facets of civil engineering. Covering topics such as damage inspection, safety risk management, and information modeling, this premier reference source is an essential resource for engineers, government officials, business leaders and executives, construction managers, students and faculty of higher education, librarians, researchers, and academicians.

The increase in air pollution and vehicular emissions has led to the development of the renewable energy-based generation and electrification of transportation. Further, the electrification shift faces an enormous challenge due to limited driving range, long charging time, and high initial cost of deployment. Firstly, there has been a discussion on renewable energy such as how wind power and solar power can be generated by wind turbines and photovoltaics, respectively, while these are intermittent in nature. The combination of these renewable energy resources with available power generation system will make electric vehicle (EV) charging sustainable and viable after the payback period. Recently, there has also been a significant discussion focused on various EV charging types and the level of power for charging to minimize the charging time. By focusing on both sustainable and renewable energy, as well as charging infrastructures and technologies, the future for EV can be explored. Developing Charging Infrastructure and Technologies for Electric Vehicles reviews and discusses the state of the art in electric vehicle charging technologies, their applications, economic, environmental, and social impact, and integration with renewable energy. This book captures the state of the art in electric vehicle charging infrastructure deployment, their applications, architectures, and relevant technologies. In addition, this book identifies potential research directions and technologies that facilitate insights on EV charging in various charging places such as smart home charging, parking EV charging, and charging stations. This book will be essential for power system architects, mechanics, electrical engineers, practitioners, developers, practitioners, researchers, academicians, and students interested in the problems and solutions to the state-of-the-art status of electric vehicles.

Engineering plays a major role in solving real-world problems, from small inconveniences to societal or global concerns around food scarcity, water shortages, environmental damage, problems in housing or infrastructure and more. In today's rapidly evolving world, the development of the latest generation of engineering and technology is crucial for maintaining productivity, innovation, and improving our overall quality of life. Intelligent Engineering Applications and Applied Sciences for Sustainability is an essential research book that serves as a compilation of cutting-edge research and advancements in engineering, science, and technology, and more importantly, how the application of these advancements will guide the path to a more sustainable future. This book focuses on intelligent engineering applications, which encompass the design and implementation of embedded technologies in various domains. It covers a wide range of fields and their influence on the Sustainable Development Goals (SDGs), fostering interdisciplinary approaches and innovative solutions, including additive manufacturing technologies, aerospace science and engineering, agricultural advancements, computer science for sustainable development, applied biosciences, applied mathematics, industrial engineering, robotics and automation, transportation, future mobility, and much more. As an academic, rigorous exploration of various disciplines, this book serves as an invaluable resource for researchers, scholars, and professionals seeking to advance the frontiers of intelligent engineering applications and applied sciences for a sustainable future.

Water, our planet's life force, faces multiple challenges in the 21st century, including surging global demand, shifting climate patterns, and the urgent need for sustainable management. Guidance, knowledge, and hope is sharply needed in academia and technology industries, and Innovations in Machine Learning and IoT for Water Management is a formidable resource to provide these necessities. This book delves into the dynamic synergy of Artificial Intelligence (AI), Machine Learning (ML), and the Internet of Things (IoT), ushering in a new era of water resource stewardship. This book embarks on a journey through the frontiers of AI and IoT, unveiling their transformative impact on water management. From the vantage point of satellite imagery analysis, it scrutinizes the Earth's vital signs, unlocking crucial insights into water resources. It chronicles the rise of AI-powered predictive analytics, a revolutionary force propelling precision water usage and conservation. This book explains how IoT can be an effective tool to increase intelligence of our water systems. The book meticulously navigates through domains as diverse as aquifer monitoring, hydropower generation optimization, and predictive analytics for water consumption. This book caters to a diverse audience, from water management experts and environmental scientists to data science aficionados and IoT enthusiasts. Engineers seeking to reimagine the future of water systems, technology enthusiasts eager to delve into AI's potential, and individuals impassioned by preserving water will all find a well-needed resource in these pages.