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| data science renewable energy: Data Science for Wind Energy Yu Ding, 2020-12-18 Data Science for Wind Energy provides an in-depth discussion on how data science methods can improve decision making for wind energy applications, near-ground wind field analysis and forecast, turbine power curve fitting and performance analysis, turbine reliability assessment, and maintenance optimization for wind turbines and wind farms. A broad set of data science methods covered, including time series models, spatio-temporal analysis, kernel regression, decision trees, kNN, splines, Bayesian inference, and importance sampling. More importantly, the data science methods are described in the context of wind energy applications, with specific wind energy examples and case studies. Please also visit the author's book site at https://aml.engr.tamu.edu/book-dswe. Features Provides an integral treatment of data science methods and wind energy applications Includes specific demonstration of particular data science methods and their use in the context of addressing wind energy needs Presents real data, case studies and computer codes from wind energy research and industrial practice Covers material based on the author's ten plus years of academic research and insights |
| data science renewable energy: Applied Data Analysis and Modeling for Energy Engineers and Scientists T. Agami Reddy, 2011-08-09 Applied Data Analysis and Modeling for Energy Engineers and Scientists fills an identified gap in engineering and science education and practice for both students and practitioners. It demonstrates how to apply concepts and methods learned in disparate courses such as mathematical modeling, probability,statistics, experimental design, regression, model building, optimization, risk analysis and decision-making to actual engineering processes and systems. The text provides a formal structure that offers a basic, broad and unified perspective,while imparting the knowledge, skills and confidence to work in data analysis and modeling. This volume uses numerous solved examples, published case studies from the author’s own research, and well-conceived problems in order to enhance comprehension levels among readers and their understanding of the “processes”along with the tools. |
| data science renewable energy: Machine Learning and Data Science in the Power Generation Industry Patrick Bangert, 2021-01-14 Machine Learning and Data Science in the Power Generation Industry explores current best practices and quantifies the value-add in developing data-oriented computational programs in the power industry, with a particular focus on thoughtfully chosen real-world case studies. It provides a set of realistic pathways for organizations seeking to develop machine learning methods, with a discussion on data selection and curation as well as organizational implementation in terms of staffing and continuing operationalization. It articulates a body of case study–driven best practices, including renewable energy sources, the smart grid, and the finances around spot markets, and forecasting. - Provides best practices on how to design and set up ML projects in power systems, including all nontechnological aspects necessary to be successful - Explores implementation pathways, explaining key ML algorithms and approaches as well as the choices that must be made, how to make them, what outcomes may be expected, and how the data must be prepared for them - Determines the specific data needs for the collection, processing, and operationalization of data within machine learning algorithms for power systems - Accompanied by numerous supporting real-world case studies, providing practical evidence of both best practices and potential pitfalls |
| data science renewable energy: Data Science Applied to Sustainability Analysis Jennifer Dunn, Prasanna Balaprakash, 2021-05-11 Data Science Applied to Sustainability Analysis focuses on the methodological considerations associated with applying this tool in analysis techniques such as lifecycle assessment and materials flow analysis. As sustainability analysts need examples of applications of big data techniques that are defensible and practical in sustainability analyses and that yield actionable results that can inform policy development, corporate supply chain management strategy, or non-governmental organization positions, this book helps answer underlying questions. In addition, it addresses the need of data science experts looking for routes to apply their skills and knowledge to domain areas. - Presents data sources that are available for application in sustainability analyses, such as market information, environmental monitoring data, social media data and satellite imagery - Includes considerations sustainability analysts must evaluate when applying big data - Features case studies illustrating the application of data science in sustainability analyses |
| data science renewable energy: Data Science of Renewable Energy Integration Yuichi Ikeda, |
| data science renewable energy: Intelligent Data Analytics for Solar Energy Prediction and Forecasting Amit Kumar Yadav, Hasmat Malik, Majed A. Alotaibi, 2026-01-01 Intelligent Data Analytics for Solar Energy Prediction and Forecasting: Advances in Resource Assessment and PV Systems Optimization explores the utilization of advanced neural networks, machine learning and data analytics techniques for solar radiation prediction, solar energy forecasting, installation and maximum power generation. The book addresses relevant input variable selection, solar resource assessment, tilt angle calculation, and electrical characteristics of PV modules, including detailed methods, coding, modeling and experimental analysis of PV power generation under outdoor conditions. It will be of interest to researchers, scientists and advanced students across solar energy, renewables, electrical engineering, AI, machine learning, computer science, information technology and engineers. In addition, R&D professionals and other industry personnel with an interest in applications of AI, machine learning, and data analytics within solar energy and energy systems will find this book to be a welcomed resource. - Presents novel intelligent techniques with step-by-step coverage for improved optimum tilt angle calculation for the installation of photovoltaic systems - Provides coding and modeling for data-driven techniques in prediction and forecasting - Covers intelligent data-driven techniques for solar energy forecasting and prediction |
| data science renewable energy: Applications of AI and IOT in Renewable Energy Rabindra Nath Shaw, Ankush Ghosh, Saad Mekhilef, Valentina Emilia Balas, 2022-02-09 Applications of AI and IOT in Renewable Energy provides a future vision of unexplored areas and applications for Artificial Intelligence and Internet of Things in sustainable energy systems. The ideas presented in this book are backed up by original, unpublished technical research results covering topics like smart solar energy systems, intelligent dc motors and energy efficiency study of electric vehicles. In all these areas and more, applications of artificial intelligence methods, including artificial neural networks, genetic algorithms, fuzzy logic and a combination of the above in hybrid systems are included. This book is designed to assist with developing low cost, smart and efficient solutions for renewable energy systems and is intended for researchers, academics and industrial communities engaged in the study and performance prediction of renewable energy systems. - Includes future applications of AI and IOT in renewable energy - Based on case studies to give each chapter real-life context - Provides advances in renewable energy using AI and IOT with technical detail and data |
| data science renewable energy: Analytics and Optimization for Renewable Energy Integration Ning Zhang, Chongqing Kang, Ershun Du, Yi Wang, 2019-02-21 The scope of this book covers the modeling and forecast of renewable energy and operation and planning of power system with renewable energy integration.The first part presents mathematical theories of stochastic mathematics; the second presents modeling and analytic techniques for renewable energy generation; the third provides solutions on how to handle the uncertainty of renewable energy in power system operation. It includes advanced stochastic unit commitment models to acquire the optimal generation schedule under uncertainty, efficient algorithms to calculate the probabilistic power, and an efficient operation strategy for renewable power plants participating in electricity markets. |
| data science renewable energy: Renewable-Energy-Driven Future Jingzheng Ren, 2020-09-16 In order to promote the sustainable development of renewable energy and renewable-energy-driven technologies, Renewable-Energy-Driven Future: Technologies, Modelling, Applications, Sustainability and Policies provides a comprehensive view of the advanced renewable technologies and the benefits of utilizing renewable energy sources.Discussing the ways for promoting the sustainable development of renewable energy from the perspectives of technology, modelling, application, sustainability and policy, this book includes the advanced renewable-energy-driven technologies, the models for renewable energy planning and integration, the innovative applications of renewable energy sources, decision-support tools for sustainability assessment and ranking of renewable energy systems, and the regulations and policies of renewable energy.This book can benefit the researchers and experts of renewable energy by helping them to have a holistic view of renewable energy. It can also benefit the policymakers and decision-makers by helping them to make informed decisions. - Presents the advanced renewable-energy-driven technologies and the innovative applications of renewable energy sources - Develops the models for the efficient use of renewable energy, decision-making and the investigation of its climate and economic benefits - Investigates the sustainability of renewable energy systems - Features the regulations and policies of renewable energy |
| data science renewable energy: Introduction to AI Techniques for Renewable Energy System Suman Lata Tripathi, Mithilesh Kumar Dubey, Vinay Rishiwal, Sanjeevikumar Padmanaban, 2021-11-25 Introduction to AI techniques for Renewable Energy System Artificial Intelligence (AI) techniques play an essential role in modeling, analysis, and prediction of the performance and control of renewable energy. The algorithms used to model, control, or predict performances of the energy systems are complicated, involving differential equations, enormous computing power, and time requirements. Instead of complex rules and mathematical routines, AI techniques can learn critical information patterns within a multidimensional information domain. Design, control, and operation of renewable energy systems require a long-term series of meteorological data such as solar radiation, temperature, or wind data. Such long-term measurements are often non-existent for most of the interest locations or, wherever they are available, they suffer from several shortcomings, like inferior quality of data, and in-sufficient long series. The book focuses on AI techniques to overcome these problems. It summarizes commonly used AI methodologies in renewal energy, with a particular emphasis on neural networks, fuzzy logic, and genetic algorithms. It outlines selected AI applications for renewable energy. In particular, it discusses methods using the AI approach for prediction and modeling of solar radiation, seizing, performances, and controls of the solar photovoltaic (PV) systems. Features Focuses on a significant area of concern to develop a foundation for the implementation of renewable energy system with intelligent techniques Showcases how researchers working on renewable energy systems can correlate their work with intelligent and machine learning approaches Highlights international standards for intelligent renewable energy systems design, reliability, and maintenance Provides insights on solar cell, biofuels, wind, and other renewable energy systems design and characterization, including the equipment for smart energy systems This book, which includes real-life examples, is aimed at undergraduate and graduate students and academicians studying AI techniques used in renewal energy systems. |
| data science renewable energy: Sustainable Energy Jefferson W. Tester, 2005 Evaluates trade-offs and uncertainties inherent in achieving sustainable energy, analyzes the major energy technologies, and provides a framework for assessing policy options. |
| data science renewable energy: Low-Temperature Energy Systems with Applications of Renewable Energy Andriy Redko, Oleksandr Redko, Ronald DiPippo, 2019-10-23 Low-Temperature Energy Systems with Applications of Renewable Energy investigates a wide variety of low-temperature energy applications in residential, commercial, institutional, and industrial areas. It addresses the basic principles that form the groundwork for more efficient energy conversion processes and includes detailed practical methods for carrying out these critical processes. This work considers new directions in the engineering use of technical thermodynamics and energy, including more in-depth studies of the use of renewable sources, and includes worked numerical examples, review questions, and practice problems to allow readers to test their own comprehension of the material. With detailed explanations, methods, models, and algorithms, Low-Temperature Energy Systems with Applications of Renewable Energy is a valuable reference for engineers and scientists in the field of renewable energy, as well as energy researchers and academics. - Features end-of chapter review sections with questions and exercises for practical study and utilization. - Presents methods for a great variety of energy applications to improve their energy operations. - Applies real-world data to demonstrate the impact of low-temperature energy systems on renewable energy use today. |
| data science renewable energy: Advanced Concepts for Renewable Energy Supply of Data Centres Jaume Salom, Thorsten Urbaneck, Eduard Oró, 2022-09-01 The rapid increase of cloud computing, high performance computing (HPC) and the vast growth in Internet and Social Media use have aroused the interest in energy consumption and the carbon footprint of Data Centres. Data Centres primarily contain electronic equipment used for data processing (servers), data storage (storage equipment), and communications (network equipment). Collectively, this equipment processes, stores, and transmits digital information and is known as information technology (IT) equipment. Advanced Concepts for Renewable Energy Supply of Data Centres introduces a number of technical solutions for the supply of power and cooling energy into Data Centres with enhanced utilisation of renewable energy sources in order to achieve low energy Data Centres. Because of the high energy density nature of these unique infrastructures, it is essential to implement energy efficiency measures and reduce consumption before introducing any renewable energy source. A holistic approach is used with the objective of integrating many technical solutions such as management of the IT (Information Technology) load, efficient electrical supply to the IT systems, Low-Ex air-conditioning systems, interaction with district heating and cooling networks, re-use of heat, free cooling (air, seawater, groundwater), optimal use of heat and cold storage, electrical storage and integration in smart grids. This book is therefore a catalogue of advanced technical concepts that could be integrated into Data Centres portfolio in order to increase the overall efficiency and the share of renewable energies in power and cooling supply. Based on dynamic energy models implemented in TRNSYS some concepts are deeply evaluated through yearly simulations. The results of the simulation are illustrated with Sankey charts, where the energy flows per year within the subsystems of each concept for a selected scenario are shown, and graphs showing the results of parametric analysis. A set of environmental metrics (as the non-renewable primary energy) and financial metrics (CAPEX and OPEX) as well of energy efficiency metrics like the well-known PUE, are described and used to evaluate the different technical concepts. |
| data science renewable energy: Data Science for Wind Energy Yu Ding, 2019-06-04 Data Science for Wind Energy provides an in-depth discussion on how data science methods can improve decision making for wind energy applications, near-ground wind field analysis and forecast, turbine power curve fitting and performance analysis, turbine reliability assessment, and maintenance optimization for wind turbines and wind farms. A broad set of data science methods covered, including time series models, spatio-temporal analysis, kernel regression, decision trees, kNN, splines, Bayesian inference, and importance sampling. More importantly, the data science methods are described in the context of wind energy applications, with specific wind energy examples and case studies. Please also visit the author’s book site at https://aml.engr.tamu.edu/book-dswe. Features Provides an integral treatment of data science methods and wind energy applications Includes specific demonstration of particular data science methods and their use in the context of addressing wind energy needs Presents real data, case studies and computer codes from wind energy research and industrial practice Covers material based on the author's ten plus years of academic research and insights |
| data science renewable energy: Renewable Energy Resources John Twidell, Tony Weir, 2006-01-16 In the years between the first and this second edition, renewable energy has come of age; it makes good sense, good government and good business. This book considers the unchanging principles of renewable energy technologies alongside modern application and case studies. In this second edition, the presentation of the fundamentals has been improved throughout, and chapters on economics and institutional factors have been added. Likewise, sections on environmental impact have been added to each technology chapter. Renewable Energy Resources supports multi-disciplinary. |
| data science renewable energy: Frontiers of Engineering National Academy of Engineering, 2018-02-22 This volume presents papers on the topics covered at the National Academy of Engineering's 2017 US Frontiers of Engineering Symposium. Every year the symposium brings together 100 outstanding young leaders in engineering to share their cutting-edge research and innovations in selected areas. The 2017 symposium was held September 25-27 at the United Technologies Research Center in East Hartford, Connecticut. The intent of this book is to convey the excitement of this unique meeting and to highlight innovative developments in engineering research and technical work. |
| data science renewable energy: Renewable Energy Crash Course Eklas Hossain, Slobodan Petrovic, 2021-06-12 This book is a concise reader-friendly introductory guide to understanding renewable energy technologies. By using simplified classroom-tested methods developed while teaching the subject to engineering students, the authors explain in simple language an otherwise complex subject in terms that enable readers to gain a rapid fundamental understanding of renewable energy, including basic principles, the different types, energy storage, grid integration, and economies. This powerful tutorial is a great resource for students, engineers, technicians, analysts, investors, and other busy professionals who need to quickly acquire a solid understanding of the science of renewable energy technology. |
| data science renewable energy: Artificial Intelligence and Internet of Things for Renewable Energy Systems Neeraj Priyadarshi, Sanjeevikumar Padmanaban, Kamal Kant Hiran, Jens Bo Holm-Nielson, Ramesh C. Bansal, 2021-11-22 This book explains the application of Artificial Intelligence and Internet of Things on green energy systems. The design of smart grids and intelligent networks enhances energy efficiency, while the collection of environmental data through sensors and their prediction through machine learning models improve the reliability of green energy systems. |
| data science renewable energy: Renewable Energy Roland Wengenmayr, Thomas Bührke, 2011-11-28 Im Zentrum vieler Debatten zum Klimawandel steht die Diskrepanz zwischen dem weltweit wachsenden Energieverbrauch auf der einen und der Begrenztheit fossiler Ressourcen auf der anderen Seite. Erneuerbare Energien werden immer wieder als Schlüssel zur Lösung dieses Problems benannt. Doch beurteilen, ob und in welchem Umfang sie dies wirklich sind, kann man nur auf der Grundlage fundierter Informationen. Genau diese bietet der vorliegende Band. Die Autoren, führende Experten ihres Fachs, erklären verständlich, wie sich aus Wind und Sonne Energie gewinnen lässt, wie geothermische Energie nutzbar gemacht werden kann oder wie Wellenkraftwerke funktionieren. Die Herausgeber, beide Autoren der Zeitschrift Physik in unserer Zeit, möchten mit diesem Buch das Fundament für einen kompetenten und ideologiefreien Austausch zu diesem so wichtigen Thema legen. Für die englischsprachige Ausgabe wurden dem Original einige Beiträge hinzugefügt, die solche mit einem Fokus auf Deutschland und Europa ersetzen. |
| data science renewable energy: Renewable Energy Systems Henrik Lund, 2014-03-24 In this new edition of Renewable Energy Systems, globally recognized renewable energy researcher and professor, Henrik Lund, sets forth a straightforward, comprehensive methodology for comparing different energy systems' abilities to integrate fluctuating and intermittent renewable energy sources. The book does this by presenting an energy system analysis methodology. The book provides the results of more than fifteen comprehensive energy system analysis studies, examines the large-scale integration of renewable energy into the present system, and presents concrete design examples derived from a dozen renewable energy systems around the globe. Renewable Energy Systems, Second Edition also undertakes the socio-political realities governing the implementation of renewable energy systems by introducing a theoretical framework approach aimed at understanding how major technological changes, such as renewable energy, can be implemented at both the national and international levels. - Provides an introduction to the technical design of renewable energy systems - Demonstrates how to analyze the feasibility and efficiency of large-scale systems to help implementers avoid costly trial and error - Addresses the socio-political challenge of implementing the shift to renewables - Features a dozen extensive case studies from around the globe that provide real-world templates for new installations |
| data science renewable energy: Artificial Intelligence in Energy and Renewable Energy Systems Soteris Kalogirou, 2007 This book presents state of the art applications of artificial intelligence in energy and renewable energy systems design and modelling. It covers such topics as solar energy, wind energy, biomass and hydrogen as well as building services systems, power generation systems, combustion processes and refrigeration. 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 dealing with modelling and performance prediction of energy and renewable energy systems. |
| data science renewable energy: Role of IoT in Green Energy Systems Ponnusamy, Vasaki, Zaman, Noor, Jung, Low Tang, Amin, Anang Hudaya Muhamad, 2021-02-05 In the era of Industry 4.0, the world is increasingly becoming smarter as everything from mobile phones to cars to TVs connects with unique addresses and communication mechanisms. However, in order to enable the smart world to be sustainable, ICT must embark into energy efficient paradigms. Green ICT is a moving factor contributing towards energy efficiency by reducing energy utilization through software or hardware procedures. Role of IoT in Green Energy Systems presents updated research trends in green technology and the latest product and application developments towards green energy. Covering topics that include energy conservation and harvesting, renewable energy, and green and underwater internet of things, this essential reference book creates further awareness of smart energy and critically examines the contributions of ICT towards green technologies. IT specialists, researchers, academicians, and students in the area of energy harvesting and energy management, and/or those working towards green energy technologies, wireless sensor networks, and smart applications will find this monograph beneficial in their studies. |
| data science renewable energy: Sustainable Developments by Artificial Intelligence and Machine Learning for Renewable Energies Krishna Kumar, Ram Shringar Rao, Omprakash Kaiwartya, Shamim Kaiser, Sanjeevikumar Padmanaban, 2022-03-18 Sustainable Developments by Artificial Intelligence and Machine Learning for Renewable Energies analyzes the changes in this energy generation shift, including issues of grid stability with variability in renewable energy vs. traditional baseload energy generation. Providing solutions to current critical environmental, economic and social issues, this book comprises various complex nonlinear interactions among different parameters to drive the integration of renewable energy into the grid. It considers how artificial intelligence and machine learning techniques are being developed to produce more reliable energy generation to optimize system performance and provide sustainable development. As the use of artificial intelligence to revolutionize the energy market and harness the potential of renewable energy is essential, this reference provides practical guidance on the application of renewable energy with AI, along with machine learning techniques and capabilities in design, modeling and for forecasting performance predictions for the optimization of renewable energy systems. It is targeted at researchers, academicians and industry professionals working in the field of renewable energy, AI, machine learning, grid Stability and energy generation. - Covers the best-performing methods and approaches for designing renewable energy systems with AI integration in a real-time environment - Gives advanced techniques for monitoring current technologies and how to efficiently utilize the energy grid spectrum - Addresses the advanced field of renewable generation, from research, impact and idea development of new applications |
| data science renewable energy: Big Data Science and Analytics for Smart Sustainable Urbanism Simon Elias Bibri, 2019-05-30 We are living at the dawn of what has been termed ‘the fourth paradigm of science,’ a scientific revolution that is marked by both the emergence of big data science and analytics, and by the increasing adoption of the underlying technologies in scientific and scholarly research practices. Everything about science development or knowledge production is fundamentally changing thanks to the ever-increasing deluge of data. This is the primary fuel of the new age, which powerful computational processes or analytics algorithms are using to generate valuable knowledge for enhanced decision-making, and deep insights pertaining to a wide variety of practical uses and applications. This book addresses the complex interplay of the scientific, technological, and social dimensions of the city, and what it entails in terms of the systemic implications for smart sustainable urbanism. In concrete terms, it explores the interdisciplinary and transdisciplinary field of smart sustainable urbanism and the unprecedented paradigmatic shifts and practical advances it is undergoing in light of big data science and analytics. This new era of science and technology embodies an unprecedentedly transformative and constitutive power—manifested not only in the form of revolutionizing science and transforming knowledge, but also in advancing social practices, producing new discourses, catalyzing major shifts, and fostering societal transitions. Of particular relevance, it is instigating a massive change in the way both smart cities and sustainable cities are studied and understood, and in how they are planned, designed, operated, managed, and governed in the face of urbanization. This relates to what has been dubbed data-driven smart sustainable urbanism, an emerging approach based on a computational understanding of city systems and processes that reduces urban life to logical and algorithmic rules and procedures, while also harnessing urban big data to provide a more holistic and integrated view or synoptic intelligence of the city. This is increasingly being directed towards improving, advancing, and maintaining the contribution of both sustainable cities and smart cities to the goals of sustainable development. This timely and multifaceted book is aimed at a broad readership. As such, it will appeal to urban scientists, data scientists, urbanists, planners, engineers, designers, policymakers, philosophers of science, and futurists, as well as all readers interested in an overview of the pivotal role of big data science and analytics in advancing every academic discipline and social practice concerned with data–intensive science and its application, particularly in relation to sustainability. |
| data science renewable energy: Fundamentals of Renewable Energy Processes Aldo Vieira da Rosa, 2009-05-07 We are hearing a LOT about renewable energy these days! But unlike most available resources on alternative energy that focus on politics and economic impacts, da Rosa's practical guide, Fundamentals of Renewable Energy Processes, is dedicated to explaining the scientific and technological principles and processes that enable energy production from safe, renewable, clean sources. Advances in the renewable energy sphere are proceeding with an unprecedented speed, and in order for the world's alarming energy challenges to be solved, solid, up-to-date resources addressing the technical aspects of renewables are essential. This new, updated 2e of da Rosa's successful book continues to give readers all the background they need to gain a thorough understanding of the most popular types of renewable energy—hydrogen, solar power, biomass, wind power, and hydropower—from the ground up. The latest advances in all these technologies are given particular attention, and are carefully contextualized to help professionals and students grasp the whys and hows behind these breakthroughs. - Discusses how and why the most popular renewable energy sources work, including wind, solar, bio and hydrogen - Provides a thorough technical grounding for all professionals and students investigating renewable energy - The new 2e of a highly regarded guide written by an internationally renowned pioneer |
| data science renewable energy: Image Processing in Renewable: Energy Resources Opportunities and Challenges Rajesh Singh, Sachin Mishra, Anita Gehlot, Mohit Srivastava, 2022-01-04 This book is a compilation of innovative work on image processing applications for renewable energy systems. The chapters in the book describe the use of neural networks in multi-direction dynamic, topographical data frameworks which are designed to account for the distinctive contemporary issues faced when managing environmentally friendly power generation systems. The topics covered include uncertainty analysis methods, computing technologies, automated control systems, performance analysis, riverfront analysis through image processing and solar power estimation methods, to name a few. The information is also complemented with a review of problems in the electric power sector in India. This book is beneficial for professionals and researchers who work on hybrid techniques of GIS, remote sensing, image processing and the implementation of these techniques for utilizing renewable energy resources. Engineers who work on advanced algorithms for renewable energy applications will also get an updated perspective about relevant innovations in this industrial sector. |
| data science renewable energy: Digital Decarbonization Varun Sivaram, 2018 As energy industries produce ever more data, firms are harnessing greater computing power, advances in data science, and increased digital connectivity to exploit that data. These trends have the potential to transform the way energy is produced, transported, and consumed. |
| data science renewable energy: Renewable Energy Forecasting Georges Kariniotakis, 2017-09-29 Renewable Energy Forecasting: From Models to Applications provides an overview of the state-of-the-art of renewable energy forecasting technology and its applications. After an introduction to the principles of meteorology and renewable energy generation, groups of chapters address forecasting models, very short-term forecasting, forecasting of extremes, and longer term forecasting. The final part of the book focuses on important applications of forecasting for power system management and in energy markets. Due to shrinking fossil fuel reserves and concerns about climate change, renewable energy holds an increasing share of the energy mix. Solar, wind, wave, and hydro energy are dependent on highly variable weather conditions, so their increased penetration will lead to strong fluctuations in the power injected into the electricity grid, which needs to be managed. Reliable, high quality forecasts of renewable power generation are therefore essential for the smooth integration of large amounts of solar, wind, wave, and hydropower into the grid as well as for the profitability and effectiveness of such renewable energy projects. - Offers comprehensive coverage of wind, solar, wave, and hydropower forecasting in one convenient volume - Addresses a topic that is growing in importance, given the increasing penetration of renewable energy in many countries - Reviews state-of-the-science techniques for renewable energy forecasting - Contains chapters on operational applications |
| data science renewable energy: Advanced Data Analytics for Power Systems Ali Tajer, Samir M. Perlaza, H. Vincent Poor, 2021-04-08 Experts in data analytics and power engineering present techniques addressing the needs of modern power systems, covering theory and applications related to power system reliability, efficiency, and security. With topics spanning large-scale and distributed optimization, statistical learning, big data analytics, graph theory, and game theory, this is an essential resource for graduate students and researchers in academia and industry with backgrounds in power systems engineering, applied mathematics, and computer science. |
| data science renewable energy: Cases on Green Energy and Sustainable Development Yang, Peter, 2019-07-26 Despite the urgent need for action, there is a widespread lack of understanding of the benefits of using green energy sources for not only reducing carbon emissions and climate change, but also for growing a sustainable economy and society. Future citizens of the world face increasing sustainability issues and need to be better prepared for energy transformation and sustainable future economic development. Cases on Green Energy and Sustainable Development is a critical research book that focuses on the important role renewable energy and energy efficiency play in energy transition and sustainable development and covers economic and promotion policies of major renewable energy and energy-efficiency technologies. Highlighting a wide range of topics such as economics, energy storage, and transportation technologies, this book is ideal for environmentalists, academicians, researchers, engineers, policymakers, and students. |
| data science renewable energy: Renewable and Alternative Energy Resources Muhammad Asif Hanif, Farwa Nadeem, Rida Tariq, Umer Rashid, 2021-11-27 Renewable and Alternative Energy Resources provides comprehensive information on the status of all renewable and non-renewable energy resources. Chapters discuss the technological developments and environmental impacts of each energy source, giving a valuable reference of up-to-date scientific progress, technical application and comparative ecological analysis of each source. In addition to understanding the process involved in generating energy, the book looks at possible merits and demerits relevant to environmental problems, highlighting the importance of the implementation of sustainable, approachable, cost effective and durable renewable energy resources. Designed to highlight relevant concepts on energy efficiency, current technologies and ongoing industrial trends, this is an ideal reference source for academics, practitioners, professionals and upper-level students interested in the latest research on renewable energy. - Discusses developments in both renewable and non-renewable energy sources - Highlights the status of exploitive, experimental studies conducted on the global status of alternative energies - Outlines novel opportunities for improving technologies for the billion-dollar renewable industry |
| data science renewable energy: Data Analytics for Renewable Energy Integration Wei Lee Woon, Zeyar Aung, Oliver Kramer, Stuart Madnick, 2017-01-18 This book constitutes revised selected papers from the 4th ECML PKDD Workshop on Data Analytics for Renewable Energy Integration, DARE 2016, held in Riva del Garda, Italy, in September 2016. The 11 papers presented in this volume were carefully reviewed and selected for inclusion in this book and handle topics such as time series forecasting, the detection of faults, cyber security, smart grid and smart cities, technology integration, demand response and many others. |
| data science renewable energy: Design, Analysis and Applications of Renewable Energy Systems Ahmad Taher Azar, Nashwa Ahmad Kamal, 2021-09-09 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 |
| data science renewable energy: Research Anthology on Clean Energy Management and Solutions Information Resources Management Association, 2021-06-25 Energy usage and consumption continue to rise globally each year, with the most efficient and cost-effective energy sources causing huge impacts to the environment. In an effort to mitigate harmful effects to the environment, implementing clean energy resources and utilizing green energy management strategies have become worldwide initiatives, with many countries from all regions quickly becoming leaders in renewable energy usage. Still, not every energy resource is without flaws. Researchers must develop effective and low-cost strategies for clean energy in order to find the balance between production and consumption. The Research Anthology on Clean Energy Management and Solutions provides in-depth research that explores strategies and techniques used in the energy production field to optimize energy efficiency in order to maintain clean and safe use while delivering ample energy coverage. The anthology also seeks solutions to energy that have not yet been optimized or are still produced in a way that is harmful to the environment. Covering topics such as hydrogen fuel cells, renewable energy, solar power, solar systems, cost savings, and climate protection, this text is essential for electrical engineers, nuclear engineers, environmentalists, managers, policymakers, government officials, professionals in the energy industry, researchers, academicians, and students looking for the latest research on clean energy management. |
| data science renewable energy: Handbook of Research on Applied Data Science and Artificial Intelligence in Business and Industry Chkoniya, Valentina, 2021-06-25 The contemporary world lives on the data produced at an unprecedented speed through social networks and the internet of things (IoT). Data has been called the new global currency, and its rise is transforming entire industries, providing a wealth of opportunities. Applied data science research is necessary to derive useful information from big data for the effective and efficient utilization to solve real-world problems. A broad analytical set allied with strong business logic is fundamental in today’s corporations. Organizations work to obtain competitive advantage by analyzing the data produced within and outside their organizational limits to support their decision-making processes. This book aims to provide an overview of the concepts, tools, and techniques behind the fields of data science and artificial intelligence (AI) applied to business and industries. The Handbook of Research on Applied Data Science and Artificial Intelligence in Business and Industry discusses all stages of data science to AI and their application to real problems across industries—from science and engineering to academia and commerce. This book brings together practice and science to build successful data solutions, showing how to uncover hidden patterns and leverage them to improve all aspects of business performance by making sense of data from both web and offline environments. Covering topics including applied AI, consumer behavior analytics, and machine learning, this text is essential for data scientists, IT specialists, managers, executives, software and computer engineers, researchers, practitioners, academicians, and students. |
| data science renewable energy: Handbook of Research on Renewable Energy and Electric Resources for Sustainable Rural Development Valeriy Kharchenko, Pandian Vasant, 2018 To maintain a healthy ecosystem for contemporary society and for future generations, policies must be implemented to protect the environment. This can be achieved by consistent evaluation of new initiatives and strategies. The Handbook of Research on Renewable Energy and Electric Resources for Sustainable Rural Development is a critical scholarly resource that examines efficient use of electric resources and renewable energy sources which have a positive impact on sustainable development. Featuring coverage on cogeneration thermal modules, photovoltaic (pv) solar, and renewable energy systems (RES) application practices, this publication is geared towards academics, practitioners, professionals, and upper-level students interested in the latest research on renewable energy and electric resources for sustainable rural development. |
| data science renewable energy: Marine Renewable Energy Technology and Environmental Interactions Mark A. Shields, Andrew I.L. Payne, 2014-02-12 It is now widely recognized that there is a need for long-term secure and suitable sustainable forms of energy. Renewable energy from the marine environment, in particular renewable energy from tidal currents, wave and wind, can help achieve a sustainable energy future. Our understanding of environmental impacts and suitable mitigation methods associated with extracting renewable energy from the marine environment is improving all the time and it is essential that we be able to distinguish between natural and anthropocentric drivers and impacts. An overview of current understanding of the environmental implications of marine renewable energy technology is provided. |
| data science renewable energy: The Changing Frontier Adam B. Jaffe, Benjamin F. Jones, 2015-08-14 In 1945, Vannevar Bush, founder of Raytheon and one-time engineering dean at MIT, delivered a report to the president of the United States that argued for the importance of public support for science, and the importance of science for the future of the nation. The report, Science: The Endless Frontier, set America on a path toward strong and well-funded institutions of science, creating an intellectual architecture that still defines scientific endeavor today. In The Changing Frontier, Adam B. Jaffe and Benjamin Jones bring together a group of prominent scholars to consider the changes in science and innovation in the ensuing decades. The contributors take on such topics as changes in the organization of scientific research, the geography of innovation, modes of entrepreneurship, and the structure of research institutions and linkages between science and innovation. An important analysis of where science stands today, The Changing Frontier will be invaluable to practitioners and policy makers alike. |
| data science renewable energy: Data Analytics for Renewable Energy Integration: Informing the Generation and Distribution of Renewable Energy Wei Lee Woon, Zeyar Aung, Oliver Kramer, Stuart Madnick, 2017-11-24 This book constitutes revised selected papers from the 5th ECML PKDD Workshop on Data Analytics for Renewable Energy Integration, DARE 2017, held in Skopje, Macedonia, in September 2017. The 11 papers presented in this volume were carefully reviewed and selected for inclusion in this book and handle topics such as time series forecasting, the detection of faults, cyber security, smart grid and smart cities, technology integration, demand response and many others. |
| data science renewable energy: Data Science Class 8 VK Jain, 2021-10-11 TAGLINE Data Science is a multidisciplinary field that also interacts with various other technologies like Artificial Intelligence, Machine Learning, Deep Learning, the Internet of Things, etc. KEY FEATURES ● National Education Policy 2020 ● Activity: This section contains a topic based practical activity for the students to explore and learn. ● Higher Order Thinking Skills: This section contains the questions that are out of the box and helps the learner to think differently. ● Glossary: This section contains definition of common data science terms. ● Applied Project: This section contains an activity that applies the concepts of the chapter in real-life. ● Digital Solutions DESCRIPTION “Touchpad” Data Science book is designed as per the latest CBSE curriculum with an inter-disciplinary approach towards Mathematics, Statistics and Information Technology. The book inculcates real-life scenarios to explain the concepts and helps the students become better Data Science literates and pursue future endeavours confidently. To enrich the subject, this book contains different types of exercises like Objective Type Questions, Standard Questions and Higher Order Thinking Skills (HOTS). This book also includes Do You Know? and Activity which helps the students to learn and think outside the box. It helps the students to think and not just memorize, at the same time improving their cognitive ability. WHAT WILL YOU LEARN You will learn about: ● Data ● Data Science ● Data Visualisation ● Data Science and Artificial Intelligence WHO THIS BOOK IS FOR Grade - 8 TABLE OF CONTENTS 1. Introduction to Data 2. Introduction to Data Science 3. Data Visualisation 4. Data Science and Artificial Intelligence 5. Projects 6. Glossary |
Data and Digital Outputs Management Plan (DDOMP)
Data and Digital Outputs Management Plan (DDOMP)
Building New Tools for Data Sharing and Reuse through a …
Jan 10, 2019 · The SEI CRA will closely link research thinking and technological innovation toward accelerating the full path of discovery-driven data use and open science. This will …
Open Data Policy and Principles - Belmont Forum
The data policy includes the following principles: Data should be: Discoverable through catalogues and search engines; Accessible as open data by default, and made available with …
Belmont Forum Adopts Open Data Principles for Environmental …
Jan 27, 2016 · Adoption of the open data policy and principles is one of five recommendations in A Place to Stand: e-Infrastructures and Data Management for Global Change Research, …
Belmont Forum Data Accessibility Statement and Policy
The DAS encourages researchers to plan for the longevity, reusability, and stability of the data attached to their research publications and results. Access to data promotes reproducibility, …
Climate-Induced Migration in Africa and Beyond: Big Data and …
CLIMB will also leverage earth observation and social media data, and combine them with survey and official statistical data. This holistic approach will allow us to analyze migration process …
Advancing Resilience in Low Income Housing Using Climate …
Jun 4, 2020 · Environmental sustainability and public health considerations will be included. Machine Learning and Big Data Analytics will be used to identify optimal disaster resilient …
Belmont Forum
What is the Belmont Forum? The Belmont Forum is an international partnership that mobilizes funding of environmental change research and accelerates its delivery to remove critical …
Waterproofing Data: Engaging Stakeholders in Sustainable Flood …
Apr 26, 2018 · Waterproofing Data investigates the governance of water-related risks, with a focus on social and cultural aspects of data practices. Typically, data flows up from local levels …
Data Management Annex (Version 1.4) - Belmont Forum
A full Data Management Plan (DMP) for an awarded Belmont Forum CRA project is a living, actively updated document that describes the data management life cycle for the data to be …
Data and Digital Outputs Management Plan (DDOMP)
Data and Digital Outputs Management Plan (DDOMP)
Building New Tools for Data Sharing and Reuse through a …
Jan 10, 2019 · The SEI CRA will closely link research thinking and technological innovation toward accelerating the full path of discovery-driven data use and open science. This will …
Open Data Policy and Principles - Belmont Forum
The data policy includes the following principles: Data should be: Discoverable through catalogues and search engines; Accessible as open data by default, and made available with …
Belmont Forum Adopts Open Data Principles for Environmental …
Jan 27, 2016 · Adoption of the open data policy and principles is one of five recommendations in A Place to Stand: e-Infrastructures and Data Management for Global Change Research, …
Belmont Forum Data Accessibility Statement and Policy
The DAS encourages researchers to plan for the longevity, reusability, and stability of the data attached to their research publications and results. Access to data promotes reproducibility, …
Climate-Induced Migration in Africa and Beyond: Big Data and …
CLIMB will also leverage earth observation and social media data, and combine them with survey and official statistical data. This holistic approach will allow us to analyze migration process …
Advancing Resilience in Low Income Housing Using Climate …
Jun 4, 2020 · Environmental sustainability and public health considerations will be included. Machine Learning and Big Data Analytics will be used to identify optimal disaster resilient …
Belmont Forum
What is the Belmont Forum? The Belmont Forum is an international partnership that mobilizes funding of environmental change research and accelerates its delivery to remove critical …
Waterproofing Data: Engaging Stakeholders in Sustainable Flood …
Apr 26, 2018 · Waterproofing Data investigates the governance of water-related risks, with a focus on social and cultural aspects of data practices. Typically, data flows up from local levels …
Data Management Annex (Version 1.4) - Belmont Forum
A full Data Management Plan (DMP) for an awarded Belmont Forum CRA project is a living, actively updated document that describes the data management life cycle for the data to be …
