Ocean Energy Modeling and Simulation with Big Data: Computational Intelligence for System Optimization and Grid Integration offers the fundamental and practical aspects of big data solutions applied to ocean and offshore energy systems. The book explores techniques for assessment of tidal, wave and offshore wind energy systems. It presents the use of data mining software to simulate systems and Hadoop technology to evaluate control systems. The use of Map Reduce algorithms in systems optimization is examined, along with the application of NoSQL in systems management. Actual data collection through web-based applications and social networks is discussed, along with practical applications of recommendations. - Introduces computational methods for processing and analyzing data to predict ocean energy system production, assess their efficiency, and ensure their reliable connection to power grids - Covers data processing solutions like Hadoop, NoSQL, Map Reduce and Lambda, discussing their applications in ocean energy for system design and optimization - Provides practical exercises that demonstrate the concepts explored in each chapter

The Internet of Energy (IoE), with the integration of advanced information and communication technologies (ICT), has led to a transformation of traditional networks to smart systems. Internet of Energy Handbook provides updated knowledge in the field of energy management with an Internet of Things (IoT) perspective. Features Explains the technological developments for energy management leading to a reduction in energy consumption through topics like smart energy systems, smart sensors, communication, techniques, and utilization Includes dedicated sections covering varied aspects related to renewable sources of energy, power distribution, and generation Incorporates energy efficiency, optimization, and sensor technologies Covers multidisciplinary aspects in computational intelligence and IoT Discusses building energy management aspects including temperature, humidity, the number of persons involved, and light intensity This handbook is aimed at graduate students, researchers, and professionals interested in power systems, IoT, smart grids, electrical engineering, and transmission.

AI-Powered IoT in the Energy Industry: Digital Technology and Sustainable Energy Systems looks at opportunities to employ cutting-edge applications of artificial intelligence (AI), the Internet of Things (IoT), and Machine Learning (ML) in designing and modeling energy and renewable energy systems. The book's main objectives are to demonstrate how big data can help with energy efficiency and demand reduction, increase the usage of renewable energy sources, and assist in transitioning from a centralized system to a distributed, efficient, and embedded energy system. Contributions cover the fundamentals of the renewable energy sector, including solar, wind, biomass, and hydrogen, as well as building services and power generation systems. Chapters also examine renewable energy property prediction methods and discuss AI and IoT prediction models for biomass thermal properties. ​Covers renewable energy sector fundamentals; Explains the application of big data in distributed energy domains; Discusses AI and IoT prediction methods and models.

Design, Analysis and Applications of Renewable Energy Systems covers recent advancements in the study of renewable energy control systems by bringing together diverse scientific breakthroughs on the modeling, control and optimization of renewable energy systems as conveyed by leading energy systems engineering researchers. The book focuses on present novel solutions for many problems in the field, covering modeling, control theorems and the optimization techniques that will help solve many scientific issues for researchers. Multidisciplinary applications are also discussed, along with their fundamentals, modeling, analysis, design, realization and experimental results. This book fills the gaps between different interdisciplinary applications, ranging from mathematical concepts, modeling, and analysis, up to the realization and experimental work. - Presents some of the latest innovative approaches to renewable energy systems from the point-of-view of dynamic modeling, system analysis, optimization, control and circuit design - Focuses on advances related to optimization techniques for renewable energy and forecasting using machine learning methods - Includes new circuits and systems, helping researchers solve many nonlinear problems

Climate change mechanisms, impacts, risks, mitigation, adaption, and governance are widely recognized as the biggest, most interconnected problem facing humanity. Big Data Mining for Climate Change addresses one of the fundamental issues facing scientists of climate or the environment: how to manage the vast amount of information available and analyse it. The resulting integrated and interdisciplinary big data mining approaches are emerging, partially with the help of the United Nation's big data climate challenge, some of which are recommended widely as new approaches for climate change research. Big Data Mining for Climate Change delivers a rich understanding of climate-related big data techniques and highlights how to navigate huge amount of climate data and resources available using big data applications. It guides future directions and will boom big-data-driven researches on modeling, diagnosing and predicting climate change and mitigating related impacts. This book mainly focuses on climate network models, deep learning techniques for climate dynamics, automated feature extraction of climate variability, and sparsification of big climate data. It also includes a revelatory exploration of big-data-driven low-carbon economy and management. Its content provides cutting-edge knowledge for scientists and advanced students studying climate change from various disciplines, including atmospheric, oceanic and environmental sciences; geography, ecology, energy, economics, management, engineering, and public policy.

Sustainable Design for Renewable Processes: Principles and Case Studies covers the basic technologies to collect and process renewable resources and raw materials and transform them into useful products. Starting with basic principles on process analysis, integration and optimization that also addresses challenges, the book then discusses applied principles using a number of examples and case studies that cover biomass, waste, solar, water and wind as resources, along with a set of technologies including gasification, pyrolysis, hydrolysis, digestion, fermentation, solar thermal, solar photovoltaics, electrolysis, energy storage, etc. The book includes examples, exercises and models using Python, Julia, MATLAB, GAMS, EXCEL, CHEMCAD or ASPEN. This book shows students the challenges posed by renewable-based processes by presenting fundamentals, case studies and step-by-step analyses of renewable resources. Hence, this is an ideal and comprehensive reference for Masters and PhD students, engineers and designers. - Addresses the fundamentals and applications of renewable energy process design for all major resources, including biomass, solar, wind, geothermal, waste and water - Provides detailed case studies, step-by-step instructions, and guidance for each renewable energy technology - Presents models and simulations for a wide variety of platforms, including state-of-the-art and open access platforms in addition to well-known commercial software

This book constitutes the revised selected papers of the 21st Smoky Mountains Computational Sciences and Engineering Conference, SMC 2021, held in Oak Ridge, TN, USA*, in October 2021. The 33 full papers and 3 short papers presented were carefully reviewed and selected from a total of 88 submissions. The papers are organized in topical sections of computational applications: converged HPC and artificial intelligence; advanced computing applications: use cases that combine multiple aspects of data and modeling; advanced computing systems and software: connecting instruments from edge to supercomputers; deploying advanced computing platforms: on the road to a converged ecosystem; scientific data challenges. *The conference was held virtually due to the COVID-19 pandemic.