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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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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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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

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This volume comprises the second part of the proceedings of the 10th International Conference on Finite Volumes for Complex Applications, FVCA, held in Strasbourg, France, during October 30 to November 3, 2023. The Finite Volume method, and several of its variants, is a spatial discretization technique for partial differential equations based on the fundamental physical principle of conservation. Recent decades have brought significant success in the theoretical understanding of the method. Many finite volume methods are also built to preserve some properties of the continuous equations, including maximum principles, dissipativity, monotone decay of the free energy, asymptotic stability, or stationary solutions. Due to these properties, finite volume methods belong to the wider class of compatible discretization methods, which preserve qualitative properties of continuous problems at the discrete level. This structural approach to the discretization of partial differential equations becomes particularly important for multiphysics and multiscale applications. In recent years, the efficient implementation of these methods in numerical software packages, more specifically to be used in supercomputers, has drawn some attention. The first volume contains all invited papers, as well as the contributed papers focusing on finite volume schemes for elliptic and parabolic problems. They include structure-preserving schemes, convergence proofs, and error estimates for problems governed by elliptic and parabolic partial differential equations. This volume is focused on finite volume methods for hyperbolic and related problems, such as methods compatible with the low Mach number limit or able to exactly preserve steady solutions, the development and analysis of high order methods, or the discretization of kinetic equations.

Mathematics. --- Engineering mathematics. --- Engineering --- Mathematical and Computational Engineering Applications. --- Data processing. --- Finite volume method.