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In the era of propelling traditional energy systems to evolve towards smart energy systems, including power generation, energy storage systems, and electricity consumption have become more dynamic. The quality and reliability of power supply are impacted by the sporadic and rising use of electric vehicles, domestic loads, and industrial loads. Similarly, with the integration of solid state devices, renewable sources, and distributed generation, power generation processes are evolving in a variety of ways. Several cutting-edge technologies are necessary for the safe and secure operation of power systems in such a dynamic setting, including load distribution, automation, energy regulation & control, and energy trading. This book covers the applications of various big data analytics,artificial intelligence, and machine learning technologies in smart grids for demand prediction, decision-making processes, policy, and energy management. The book delves into the new technologies for modern power systems such as the Internet of Things, Blockchain for smart home and smart city solutions in depth.
Artificial intelligence --- Smart power grids --- Engineering applications
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ARTIFICIAL INTELLIGENCE FOR RENEWABLE ENERGY SYSTEMS Renewable energy systems, including solar, wind, biodiesel, hybrid energy, and other relevant types, have numerous advantages compared to their conventional counterparts. This book presents the application of machine learning and deep learning techniques for renewable energy system modeling, forecasting, and optimization for efficient system design. Due to the importance of renewable energy in today's world, this book was designed to enhance the reader's knowledge based on current developments in the field. For instance, the extraction and selection of machine learning algorithms for renewable energy systems, forecasting of wind and solar radiation are featured in the book. Also highlighted are intelligent data, renewable energy informatics systems based on supervisory control and data acquisition (SCADA); and intelligent condition monitoring of solar and wind energy systems. Moreover, an AI-based system for real-time decision-making for renewable energy systems is presented; and also demonstrated is the prediction of energy consumption in green buildings using machine learning. The chapter authors also provide both experimental and real datasets with great potential in the renewable energy sector, which apply machine learning (ML) and deep learning (DL) algorithms that will be helpful for economic and environmental forecasting of the renewable energy business. Audience The primary target audience includes research scholars, industry engineers, and graduate students working in renewable energy, electrical engineering, machine learning, information & communication technology.
Renewable energy sources --- Artificial intelligence --- Data processing. --- Engineering applications.
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'Intelligent Transportation Infrastructure' (ITI) is an open access, online only journal publishing cutting-edge and innovative research to act as a bridge between advances being made in artificial intelligence and transportation infrastructure engineering.
Civil engineering --- Artificial intelligence --- Data processing --- Engineering applications
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The text presents the basic understanding of the machine learning algorithms used for communication networks in a single volume. It will serve as an ideal reference text for senior undergraduate, graduate students, and academic researchers in diverse engineering domains including electrical, electronics and communication, computer.
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Unmanned aerial systems (UAS) have evolved rapidly in recent years thanks to advances in microelectromechanical components, navigation, perception, and artificial intelligence, allowing for a fast development of autonomy. This book presents general approaches to develop, test, and evaluate critical functions such as navigation, obstacle avoidance and perception, and the capacity to improve performance in real and simulated scenarios. It provides the practical knowledge to install, analyze and evaluate UAS solutions working in real systems; illustrates how to use and configure complete platforms and software tools; and reviews the main enabling technologies applied to develop UAS, possibilities and evaluation methodology. You will get the tools you need to evaluate navigation and obstacle avoidance functions, object detection, and planning and landing alternatives in simulated conditions. The book also provides helpful guidance on the integration of additional sensors (video, weather, meteorological) and communication networks to build IoT solutions. This is an important book for practitioners and researchers interested in integrating advanced techniques in the fields of AI, sensor fusion and mission management, and anyone interest in applying and testing advanced algorithms in UAS platforms.
Vehicles, Remotely piloted. --- Robotics. --- Drone aircraft. --- Artificial intelligence --- Engineering applications.
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Artificial intelligence --- Mathematical optimization --- Engineering applications --- Data processing
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This book offers practical guidance on how to implement data-driven, accelerated product development through concepts, challenges, and applications. It describes activities related to creating new or improved functional material products by discovering new ingredients or new ingredient combinations resulting in targeted quality properties.
New products. --- Product design --- Artificial intelligence --- Data processing. --- Engineering applications.
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This book has included the following major sections: ""Introduction"", ""History of Biochar,"" ""Preparation of Biochar,"" and ""Applications of Biochar."" The editor and authors hope that the development of biochar can cross its application field from agriculture into engineering.
Biochar. --- Engineering applications. --- Biomass energy --- Charcoal --- Engineering --- Soil Science --- Physical Sciences --- Engineering and Technology --- Environmental Engineering
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Electric power systems --- Artificial intelligence --- Data processing. --- Engineering applications. --- Engineering --- Data processing
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Electric power systems --- Artificial intelligence --- Data processing. --- Engineering applications. --- Engineering --- Data processing