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Data Science Of Renewable Energy Integration
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Book Synopsis Data Analytics for Renewable Energy Integration by : Wei Lee Woon
Download or read book Data Analytics for Renewable Energy Integration written by Wei Lee Woon and published by Springer. This book was released on 2014-11-20 with total page 151 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book constitutes revised selected papers from the second ECML PKDD Workshop on Data Analytics for Renewable Energy Integration, DARE 2014, held in Nancy, France, in September 2014. The 11 papers presented in this volume were carefully reviewed and selected for inclusion in this book.
Book Synopsis Data Analytics for Renewable Energy Integration by : Wei Lee Woon
Download or read book Data Analytics for Renewable Energy Integration written by Wei Lee Woon and published by Springer. This book was released on 2015-12-14 with total page 155 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book constitutes revised selected papers from the third ECML PKDD Workshop on Data Analytics for Renewable Energy Integration, DARE 2015, held in Porto, Portugal, in September 2015. The 10 papers presented in this volume were carefully reviewed and selected for inclusion in this book.
Book Synopsis Data Analytics for Renewable Energy Integration: Informing the Generation and Distribution of Renewable Energy by : Wei Lee Woon
Download or read book Data Analytics for Renewable Energy Integration: Informing the Generation and Distribution of Renewable Energy written by Wei Lee Woon and published by Springer. This book was released on 2017-11-24 with total page 142 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.
Book Synopsis Data Analytics for Renewable Energy Integration by : Wei Lee Woon
Download or read book Data Analytics for Renewable Energy Integration written by Wei Lee Woon and published by Springer. This book was released on 2017-01-18 with total page 137 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.
Book Synopsis Data Science of Renewable Energy Integration by : Yuichi Ikeda
Download or read book Data Science of Renewable Energy Integration written by Yuichi Ikeda and published by Springer Nature. This book was released on with total page 325 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Book Synopsis Data Analytics for Renewable Energy Integration. Technologies, Systems and Society by : Wei Lee Woon
Download or read book Data Analytics for Renewable Energy Integration. Technologies, Systems and Society written by Wei Lee Woon and published by Springer. This book was released on 2018-11-16 with total page 167 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book constitutes the revised selected papers from the 6th ECML PKDD Workshop on Data Analytics for Renewable Energy Integration, DARE 2018, held in Dublin, Ireland, in September 2018. The 9 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.
Book Synopsis Spatio-Temporal Data Analytics for Wind Energy Integration by : Lei Yang
Download or read book Spatio-Temporal Data Analytics for Wind Energy Integration written by Lei Yang and published by Springer. This book was released on 2014-11-14 with total page 80 pages. Available in PDF, EPUB and Kindle. Book excerpt: This SpringerBrief presents spatio-temporal data analytics for wind energy integration using stochastic modeling and optimization methods. It explores techniques for efficiently integrating renewable energy generation into bulk power grids. The operational challenges of wind, and its variability are carefully examined. A spatio-temporal analysis approach enables the authors to develop Markov-chain-based short-term forecasts of wind farm power generation. To deal with the wind ramp dynamics, a support vector machine enhanced Markov model is introduced. The stochastic optimization of economic dispatch (ED) and interruptible load management are investigated as well. Spatio-Temporal Data Analytics for Wind Energy Integration is valuable for researchers and professionals working towards renewable energy integration. Advanced-level students studying electrical, computer and energy engineering should also find the content useful.
Book Synopsis Analytics and Optimization for Renewable Energy Integration by : Ning Zhang
Download or read book Analytics and Optimization for Renewable Energy Integration written by Ning Zhang and published by CRC Press. This book was released on 2019-02-21 with total page 372 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.
Book Synopsis Machine Learning and Data Science in the Power Generation Industry by : Patrick Bangert
Download or read book Machine Learning and Data Science in the Power Generation Industry written by Patrick Bangert and published by Elsevier. This book was released on 2021-01-14 with total page 276 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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
Book Synopsis Renewable Energy Integration to the Grid by : Neeraj Gupta
Download or read book Renewable Energy Integration to the Grid written by Neeraj Gupta and published by CRC Press. This book was released on 2022-03-24 with total page 275 pages. Available in PDF, EPUB and Kindle. Book excerpt: Presents analysis and modeling of renewable generation uncertainty for planning and operation, beneficial for industry professionals and researchers. Discusses the dependence modeling of multi-site renewable generations in detail. Covers probabilistic analysis, useful for data analysts. Discusses various aspects of renewable energy integration i.e. technical, economic, etc. Covers correlation factors and methodologies are validated with case studies with various standard test systems.
Book Synopsis Data Science and Applications for Modern Power Systems by : Le Xie
Download or read book Data Science and Applications for Modern Power Systems written by Le Xie and published by Springer Nature. This book was released on 2023-06-20 with total page 446 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book offers a comprehensive collection of research articles that utilize data—in particular large data sets—in modern power systems operation and planning. As the power industry moves towards actively utilizing distributed resources with advanced technologies and incentives, it is becoming increasingly important to benefit from the available heterogeneous data sets for improved decision-making. The authors present a first-of-its-kind comprehensive review of big data opportunities and challenges in the smart grid industry. This book provides succinct and useful theory, practical algorithms, and case studies to improve power grid operations and planning utilizing big data, making it a useful graduate-level reference for students, faculty, and practitioners on the future grid.
Book Synopsis Intelligent Data Analytics for Power and Energy Systems by : Hasmat Malik
Download or read book Intelligent Data Analytics for Power and Energy Systems written by Hasmat Malik and published by Springer Nature. This book was released on 2022-02-17 with total page 649 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book brings together state-of-the-art advances in intelligent data analytics as driver of the future evolution of PaE systems. In the modern power and energy (PaE) domain, the increasing penetration of renewable energy sources (RES) and the consequent empowerment of consumers as a central and active solution to deal with the generation and development variability are driving the PaE system towards a historic paradigm shift. The small-scale, diversity, and especially the number of new players involved in the PaE system potentiate a significant growth of generated data. Moreover, advances in communication (between IoT devices and M2M: machine to machine, man to machine, etc.) and digitalization hugely increased the volume of data that results from PaE components, installations, and systems operation. This data is becoming more and more important for PaE systems operation, maintenance, planning, and scheduling with relevant impact on all involved entities, from producers, consumer,s and aggregators to market and system operators. However, although the PaE community is fully aware of the intrinsic value of those data, the methods to deal with it still necessitate substantial enhancements, development and research. Intelligent data analytics is thereby playing a fundamental role in this domain, by enabling stakeholders to expand their decision-making method and achieve the awareness on the PaE environment. The editors also included demonstrated codes for presented problems for better understanding for beginners.
Book Synopsis Data Science for Wind Energy by : Yu Ding
Download or read book Data Science for Wind Energy written by Yu Ding and published by CRC Press. This book was released on 2019-06-04 with total page 400 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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
Book Synopsis Machine Learning for Sustainable Development by : Kamal Kant Hiran
Download or read book Machine Learning for Sustainable Development written by Kamal Kant Hiran and published by Walter de Gruyter GmbH & Co KG. This book was released on 2021-07-19 with total page 214 pages. Available in PDF, EPUB and Kindle. Book excerpt: The book will focus on the applications of machine learning for sustainable development. Machine learning (ML) is an emerging technique whose diffusion and adoption in various sectors (such as energy, agriculture, internet of things, infrastructure) will be of enormous benefit. The state of the art of machine learning models is most useful for forecasting and prediction of various sectors for sustainable development.
Book Synopsis The Global Impact of Renewable Energy and Data Analytics by : Kingsley Onyeagusi
Download or read book The Global Impact of Renewable Energy and Data Analytics written by Kingsley Onyeagusi and published by GRIN Verlag. This book was released on 2024-01-08 with total page 19 pages. Available in PDF, EPUB and Kindle. Book excerpt: Academic Paper from the year 2023 in the subject Politics - Environmental Policy, , language: English, abstract: This article explores the critical role of renewable energy and intelligence systems in developing countries seeking to expand energy access and for developed nations working to decarbonise energy systems. The opportunities, challenges, and impacts of the renewable's revolution vary between poor nations with limited existing infrastructure and rich countries possessing advanced technical capabilities. However, data-driven solutions are invaluable in maximising clean energy potential everywhere while managing variability. By comparing and contrasting the nuances of integrating high shares of solar, wind, and other renewables onto grids in Asia, Africa, the Americas, and Europe, insights and best practices can be shared across borders. Artificial intelligence and machine learning are unlocking the promise of renewable energy worldwide through sophisticated forecasting of supply and demand, optimal location of projects, predictive maintenance of assets, and real-time management of complex systems. However, technology gaps and a lack of technical expertise hamper many developing nations. Targeted financing, capacity building, and knowledge transfer are critical to empowering these regions to benefit from data and renewables in providing affordable, reliable, and sustainable energy access. This article highlights significant trends, analyses case studies of success, and synthesises expert perspectives across the developed and developing world. By documenting the global impacts of renewables and analytics, stakeholders ranging from policymakers to investors can make informed decisions that steer all nations towards a decarbonised energy future that leaves no one behind. The insights can help guide an inclusive and just transition worldwide.
Book Synopsis Data Science and Big Data Analytics in Smart Environments by : Marta Chinnici
Download or read book Data Science and Big Data Analytics in Smart Environments written by Marta Chinnici and published by CRC Press. This book was released on 2021-07-28 with total page 305 pages. Available in PDF, EPUB and Kindle. Book excerpt: Most applications generate large datasets, like social networking and social influence programs, smart cities applications, smart house environments, Cloud applications, public web sites, scientific experiments and simulations, data warehouse, monitoring platforms, and e-government services. Data grows rapidly, since applications produce continuously increasing volumes of both unstructured and structured data. Large-scale interconnected systems aim to aggregate and efficiently exploit the power of widely distributed resources. In this context, major solutions for scalability, mobility, reliability, fault tolerance and security are required to achieve high performance and to create a smart environment. The impact on data processing, transfer and storage is the need to re-evaluate the approaches and solutions to better answer the user needs. A variety of solutions for specific applications and platforms exist so a thorough and systematic analysis of existing solutions for data science, data analytics, methods and algorithms used in Big Data processing and storage environments is significant in designing and implementing a smart environment. Fundamental issues pertaining to smart environments (smart cities, ambient assisted leaving, smart houses, green houses, cyber physical systems, etc.) are reviewed. Most of the current efforts still do not adequately address the heterogeneity of different distributed systems, the interoperability between them, and the systems resilience. This book will primarily encompass practical approaches that promote research in all aspects of data processing, data analytics, data processing in different type of systems: Cluster Computing, Grid Computing, Peer-to-Peer, Cloud/Edge/Fog Computing, all involving elements of heterogeneity, having a large variety of tools and software to manage them. The main role of resource management techniques in this domain is to create the suitable frameworks for development of applications and deployment in smart environments, with respect to high performance. The book focuses on topics covering algorithms, architectures, management models, high performance computing techniques and large-scale distributed systems.
Book Synopsis Renewable Energy Integration by : Lawrence E. Jones
Download or read book Renewable Energy Integration written by Lawrence E. Jones and published by Academic Press. This book was released on 2014-06-12 with total page 474 pages. Available in PDF, EPUB and Kindle. Book excerpt: Renewable Energy Integration is a ground-breaking new resource - the first to offer a distilled examination of the intricacies of integrating renewables into the power grid and electricity markets. It offers informed perspectives from internationally renowned experts on the challenges to be met and solutions based on demonstrated best practices developed by operators around the world. The book's focus on practical implementation of strategies provides real-world context for theoretical underpinnings and the development of supporting policy frameworks. The book considers a myriad of wind, solar, wave and tidal integration issues, thus ensuring that grid operators with low or high penetration of renewable generation can leverage the victories achieved by their peers. Renewable Energy Integration highlights, carefully explains, and illustrates the benefits of advanced technologies and systems for coping with variability, uncertainty, and flexibility. Lays out the key issues around the integration of renewables into power grids and markets, from the intricacies of operational and planning considerations, to supporting regulatory and policy frameworks Provides global case studies that highlight the challenges of renewables integration and present field-tested solutions Illustrates enabling and disruptive technologies to support the management of variability, uncertainty and flexibility
