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| data science for wind 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 for wind 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 for wind energy: Wind Energy Mathew Sathyajith, 2006-03-14 Growing energy demand and environmental consciousness have re-evoked human interest in wind energy. As a result, wind is the fastest growing energy source in the world today. Policy frame works and action plans have already been for- lated at various corners for meeting at least 20 per cent of the global energy - mand with new-renewables by 2010, among which wind is going to be the major player. In view of the rapid growth of wind industry, Universities, all around the world, have given due emphasis to wind energy technology in their undergraduate and graduate curriculum. These academic programmes attract students from diver- fied backgrounds, ranging from social science to engineering and technology. Fundamentals of wind energy conversion, which is discussed in the preliminary chapters of this book, have these students as the target group. Advanced resource analysis tools derived and applied are beneficial to academics and researchers working in this area. The Wind Energy Resource Analysis (WERA) software, provided with the book, is an effective tool for wind energy practitioners for - sessing the energy potential and simulating turbine performance at prospective sites. |
| data science for wind energy: Spatio-Temporal Data Analytics for Wind Energy Integration Lei Yang, Miao He, Junshan Zhang, Vijay Vittal, 2014-11-14 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. |
| data science for wind 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 for wind energy: Wind Science and Engineering Giovanni Solari, 2019-07-12 This book provides an essential overview of wind science and engineering, taking readers on a journey through the origins, developments, fundamentals, recent advancements and latest trends in this broad field. Along the way, it addresses a diverse range of topics, including: atmospheric physics; meteorology; micrometeorology; climatology; the aerodynamics of buildings, aircraft, sailing boats, road vehicles and trains; wind energy; atmospheric pollution; soil erosion; snow drift, windbreaks and crops; bioclimatic city-planning and architecture; wind actions and effects on structures; and wind hazards, vulnerability and risk. In order to provide a comprehensive overview of wind and its manifold effects, the book combines scientific, descriptive and narrative chapters. The book is chiefly intended for students and lecturers, for those who want to learn about the genesis and evolution of this topic, and for the multitude of scholars whose work involves the wind. |
| data science for wind energy: Fault Diagnosis and Sustainable Control of Wind Turbines Silvio Simani, Saverio Farsoni, 2018-01-02 Fault Diagnosis and Sustainable Control of Wind Turbines: Robust Data-Driven and Model-Based Strategies discusses the development of reliable and robust fault diagnosis and fault-tolerant ('sustainable') control schemes by means of data-driven and model-based approaches. These strategies are able to cope with unknown nonlinear systems and noisy measurements. The book also discusses simpler solutions relying on data-driven and model-based methodologies, which are key when on-line implementations are considered for the proposed schemes. The book targets both professional engineers working in industry and researchers in academic and scientific institutions. In order to improve the safety, reliability and efficiency of wind turbine systems, thus avoiding expensive unplanned maintenance, the accommodation of faults in their early occurrence is fundamental. To highlight the potential of the proposed methods in real applications, hardware–in–the–loop test facilities (representing realistic wind turbine systems) are considered to analyze the digital implementation of the designed solutions. The achieved results show that the developed schemes are able to maintain the desired performances, thus validating their reliability and viability in real-time implementations. Different groups of readers—ranging from industrial engineers wishing to gain insight into the applications' potential of new fault diagnosis and sustainable control methods, to the academic control community looking for new problems to tackle—will find much to learn from this work. - Provides wind turbine models with varying complexity, as well as the solutions proposed and developed by the authors - Addresses in detail the design, development and realistic implementation of fault diagnosis and fault tolerant control strategies for wind turbine systems - Addresses the development of sustainable control solutions that, in general, do not require the introduction of further or redundant measurements - Proposes active fault tolerant ('sustainable') solutions that are able to maintain the wind turbine working conditions with gracefully degraded performance before required maintenance can occur - Presents full coverage of the diagnosis and fault tolerant control problem, starting from the modeling and identification and finishing with diagnosis and fault tolerant control approaches - Provides MATLAB and Simulink codes for the solutions proposed |
| data science for wind energy: Data Science of Renewable Energy Integration Yuichi Ikeda, |
| data science for wind energy: Wind Energy Explained James F. Manwell, Jon G. McGowan, Anthony L. Rogers, 2010-09-14 Wind energy’s bestselling textbook- fully revised. This must-have second edition includes up-to-date data, diagrams, illustrations and thorough new material on: the fundamentals of wind turbine aerodynamics; wind turbine testing and modelling; wind turbine design standards; offshore wind energy; special purpose applications, such as energy storage and fuel production. Fifty additional homework problems and a new appendix on data processing make this comprehensive edition perfect for engineering students. This book offers a complete examination of one of the most promising sources of renewable energy and is a great introduction to this cross-disciplinary field for practising engineers. “provides a wealth of information and is an excellent reference book for people interested in the subject of wind energy.” (IEEE Power & Energy Magazine, November/December 2003) “deserves a place in the library of every university and college where renewable energy is taught.” (The International Journal of Electrical Engineering Education, Vol.41, No.2 April 2004) “a very comprehensive and well-organized treatment of the current status of wind power.” (Choice, Vol. 40, No. 4, December 2002) |
| data science for wind energy: Energy Island Allan Drummond, 2011-03 Tells how the people of Danish island of Samso decided to use wind energy to power their lives and became the Energy Island. |
| data science for wind energy: Advances in Wind Energy Conversion Technology Mathew Sathyajith, Geeta Susan Philip, 2011-04-29 With an annual growth rate of over 35%, wind is the fastest growing energy source in the world today. As a result of intensive research and developmental efforts, the technology of generating energy from wind has significantly changed during the past five years. The book brings together all the latest aspects of wind energy conversion technology - right from the wind resource analysis to grid integration of the wind generated electricity. The chapters are contributed by academic and industrial experts having vast experience in these areas. Each chapter begins with an introduction explaining the current status of the technology and proceeds further to the advanced lever to cater for the needs of readers from different subject backgrounds. Extensive bibliography/references appended to each chapter give further guidance to the interested readers. |
| data science for wind energy: Wind Energy Explained J. F. Manwell, J. G. McGowan, Anthony L. Rogers, 2002-06-21 This textbook is intended to provide an introduction to the cross-disciplinary field of wind engineering. It includes end-of-chapter tutorial sections (solutions manual available) and combines both academic and industrial experience. |
| data science for wind energy: Wind Power Generation Paul Breeze, 2016-01-21 Wind Power Generation is a concise, up-to-date and readable guide providing an introduction to one of the leading renewable power generation technologies. It includes detailed descriptions of on and offshore generation systems, and demystifies the relevant wind energy technology functions in practice as well as exploring the economic and environmental risk factors. Engineers, managers, policymakers and those involved in planning and delivering energy resources will find this reference a valuable guide, to help establish a reliable power supply address social and economic objectives. - Focuses on the evolution and developments in wind energy generation - Evaluates the economic and environmental viability of the systems with concise diagrams and accessible explanations |
| data science for wind energy: Wind Energy Systems Mario Garcia-Sanz, Constantine H. Houpis, 2012-02-02 Presenting the latest developments in the field, Wind Energy Systems: Control Engineering Design offers a novel take on advanced control engineering design techniques for wind turbine applications. The book introduces concurrent quantitative engineering techniques for the design of highly efficient and reliable controllers, which can be used to solve the most critical problems of multi-megawatt wind energy systems. This book is based on the authors’ experience during the last two decades designing commercial multi-megawatt wind turbines and control systems for industry leaders, including NASA and the European Space Agency. This work is their response to the urgent need for a truly reliable concurrent engineering methodology for the design of advanced control systems. Outlining a roadmap for such a coordinated architecture, the authors consider the links between all aspects of a multi-megawatt wind energy project, in which the wind turbine and the control system must be cooperatively designed to achieve an optimized, reliable, and successful system. Look inside for information about the QFT Control Toolbox for Matlab, the software developed by the author to facilitate the QFT robust control design (see also the link at codypower.com). The textbook’s big-picture insights can help students and practicing engineers control and optimize a wind energy system, in which large, flexible, aerodynamic structures are connected to a demanding variable electrical grid and work automatically under very turbulent and unpredictable environmental conditions. The book covers topics including robust QFT control, aerodynamics, mechanical and electrical dynamic modeling, economics, reliability, and efficiency. It also addresses standards, certification, implementation, grid integration, and power quality, as well as environmental and maintenance issues. To reinforce understanding, the authors present real examples of experimentation with commercial multi-megawatt direct-drive wind turbines, as well as on-shore, offshore, floating, and airborne wind turbine applications. They also offer a unique in-depth exploration of the quantitative feedback theory (QFT)—a proven, successful robust control technique for real-world applications—as well as advanced switching control techniques that help engineers exceed classical linear limitations. |
| data science for wind energy: Foundations of Data Science Avrim Blum, John Hopcroft, Ravindran Kannan, 2020-01-23 This book provides an introduction to the mathematical and algorithmic foundations of data science, including machine learning, high-dimensional geometry, and analysis of large networks. Topics include the counterintuitive nature of data in high dimensions, important linear algebraic techniques such as singular value decomposition, the theory of random walks and Markov chains, the fundamentals of and important algorithms for machine learning, algorithms and analysis for clustering, probabilistic models for large networks, representation learning including topic modelling and non-negative matrix factorization, wavelets and compressed sensing. Important probabilistic techniques are developed including the law of large numbers, tail inequalities, analysis of random projections, generalization guarantees in machine learning, and moment methods for analysis of phase transitions in large random graphs. Additionally, important structural and complexity measures are discussed such as matrix norms and VC-dimension. This book is suitable for both undergraduate and graduate courses in the design and analysis of algorithms for data. |
| data science for wind 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 for wind energy: Wind Resource Assessment Michael Brower, 2012-06-19 A practical, authoritative guide to the assessment of wind resources for utility-scale wind projects authored by a team of experts from a leading renewable energy consultancy The successful development of wind energy projects depends on an accurate assessment of where, how often, and how strongly the wind blows. A mistake in this stage of evaluation can cause severe financial losses and missed opportunities for developers, lenders, and investors. Wind Resource Assessment: A Practical Guide to Developing a Wind Project shows readers how to achieve a high standard of resource assessment, reduce the uncertainty associated with long-term energy performance, and maximize the value of their project assets. Beginning with the siting, installation, and operation of a high-quality wind monitoring program, this book continues with methods of data quality control and validation, extrapolating measurements from anemometer height to turbine height, adjusting short-term observations for historical climate conditions, and wind flow modeling to account for terrain and surface conditions. In addition, Wind Resource Assessment addresses special topics such as: Worker safety Data security Remote sensing technology (sodar and lidar) Offshore resource assessment Impacts of climate change Uncertainty estimation Plant design and energy production estimatio Filled with important information ranging from basic fundamentals of wind to cutting-edge research topics, and accompanied by helpful references and discussion questions, this comprehensive text designed for an international audience is a vital reference that promotes consistent standards for wind assessment across the industry. |
| data science for wind energy: Wind Energy Meteorology Stefan Emeis, 2018-03-30 This book offers an introduction to the meteorological boundary conditions for power generation from wind – both onshore and offshore, and provides meteorological information for the planning and running of this important renewable energy source. It includes the derivation of wind laws and wind-profile descriptions, especially those above the logarithmic surface layer, and discusses winds over complex terrains and nocturnal low-level jets. This updated and expanded second edition features new chapters devoted to the efficiency of large wind parks and their wakes and to offshore wind energy. |
| data science for wind energy: Multimodal and Tensor Data Analytics for Industrial Systems Improvement Nathan Gaw, |
| data science for wind energy: Wind Turbines Erich Hau, 2005-12-12 Wind Turbines addresses all those professionally involved in research, development, manufacture and operation of wind turbines. It provides a cross-disciplinary overview of modern wind turbine technology and an orientation in the associated technical, economic and environmental fields. It is based on the author's experience gained over decades designing wind energy converters with a major industrial manufacturer and, more recently, in technical consulting and in the planning of large wind park installations, with special attention to economics. The second edition accounts for the emerging concerns over increasing numbers of installed wind turbines. In particular, an important new chapter has been added which deals with offshore wind utilisation. All advanced chapters have been extensively revised and in some cases considerably extended |
| data science for wind energy: Data Analytics for Renewable Energy Integration. Technologies, Systems and Society Wei Lee Woon, Zeyar Aung, Alejandro Catalina Feliú, Stuart Madnick, 2018-11-16 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. |
| data science for wind energy: Homebrew Wind Power Dan Bartmann, Dan Fink, 2009 An illustrated guide to building and installing a wind turbine and understanding how the energy in moving air is transformed into electricity. |
| data science for wind 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 for wind energy: Theory and Application of Reuse, Integration, and Data Science Thouraya Bouabana-Tebibel, Lydia Bouzar-Benlabiod, Stuart H. Rubin, 2019-05-07 This book presents recent research in the field of reuse and integration, and will help researchers and practitioners alike to understand how they can implement reuse in different stages of software development and in various domains, from robotics and security authentication to environmental issues. Indeed, reuse is not only confined to reusing code; it can be included in every software development step. The challenge today is more about adapting solutions from one language to another, or from one domain to another. The relative validation of the reused artifacts in their new environment is also necessary, at time even critical. The book includes high-quality research papers on these and many other aspects, written by experts in information reuse and integration, who cover the latest advances in the field. Their contributions are extended versions of the best papers presented at the IEEE International Conference on Information Reuse and Integration (IRI) and IEEE International Workshop on Formal Methods Integration (FMI), which were held in San Diego in August 2017. |
| data science for wind energy: The Wind Farm Scam John R. Etherington, 2009 This book argues that the drawbacks of wind power far outweigh the advantages. Wind turbines cannot generate enough energy to reduce global CO2 levels to a meaningful degree; what's more, wind power cannot generate a steady output, necessitating back-up coal and gas power plants that significantly negate the saving of greenhouse gas emissions. In a |
| data science for wind energy: Innovation in Energy Systems Taha Selim Ustun, 2019-11-27 It has been a little over a century since the inception of interconnected networks and little has changed in the way that they are operated. Demand-supply balance methods, protection schemes, business models for electric power companies, and future development considerations have remained the same until very recently. Distributed generators, storage devices, and electric vehicles have become widespread and disrupted century-old bulk generation - bulk transmission operation. Distribution networks are no longer passive networks and now contribute to power generation. Old billing and energy trading schemes cannot accommodate this change and need revision. Furthermore, bidirectional power flow is an unprecedented phenomenon in distribution networks and traditional protection schemes require a thorough fix for proper operation. This book aims to cover new technologies, methods, and approaches developed to meet the needs of this changing field. |
| data science for wind energy: Wind Energy Engineering, Second Edition Pramod Jain, 2016-01-05 A fully up-to-date, comprehensive wind energy engineering resource This thoroughly updated reference offers complete details on effectively harnessing wind energy as a viable and economical power source. Globally recognized wind expert Pramod Jain clearly explains physics, meteorology, aerodynamics, wind measurement, wind turbines, and electricity. New energy policies and grid integration procedures are covered, including pre-deployment studies and grid modifications. Filled with diagrams, tables, charts, graphs, and statistics, Wind Energy Engineering, Second Edition, is a definitive guide to current developments and emerging technologies in wind energy. Wind Energy Engineering, Second Edition covers: The worldwide business of wind energy Wind energy basics Meteorological properties of wind and air Wind turbine aerodynamics Turbine blade element models and power curves Wind measurement and reporting Wind resource assessment Advanced resource assessment topics, including wake, losses, and uncertainty Wind turbine generator components Electricity and generator fundamentals Grid integration of wind energy Environmental impact of wind projects Financial modeling, planning, and execution of wind projects Wind energy policy and licensing guidelines |
| data science for wind energy: Supervised Machine Learning in Wind Forecasting and Ramp Event Prediction Harsh S. Dhiman, Dipankar Deb, Valentina Emilia Balas, 2020-01-31 Supervised Machine Learning in Wind Forecasting and Ramp Event Prediction provides an up-to- date overview on the broad area of wind generation and forecasting, with a focus on the role and need of Machine Learning in this emerging field of knowledge. Various regression models and signal decomposition techniques are presented and analyzed, including least-square, twin support and random forest regression, all with supervised Machine Learning. The specific topics of ramp event prediction and wake interactions are addressed in this book, along with forecasted performance. Wind speed forecasting has become an essential component to ensure power system security, reliability and safe operation, making this reference useful for all researchers and professionals researching renewable energy, wind energy forecasting and generation. |
| data science for wind energy: Data Science for Nano Image Analysis Chiwoo Park, Yu Ding, 2021-07-31 This book combines two distinctive topics: data science/image analysis and materials science. The purpose of this book is to show what type of nano material problems can be better solved by which set of data science methods. The majority of material science research is thus far carried out by domain-specific experts in material engineering, chemistry/chemical engineering, and mechanical & aerospace engineering. The book could benefit materials scientists and manufacturing engineers who were not exposed to systematic data science training while in schools, or data scientists in computer science or statistics disciplines who want to work on material image problems or contribute to materials discovery and optimization. This book provides in-depth discussions of how data science and operations research methods can help and improve nano image analysis, automating the otherwise manual and time-consuming operations for material engineering and enhancing decision making for nano material exploration. A broad set of data science methods are covered, including the representations of images, shape analysis, image pattern analysis, and analysis of streaming images, change points detection, graphical methods, and real-time dynamic modeling and object tracking. The data science methods are described in the context of nano image applications, with specific material science case studies. |
| data science for wind energy: Modeling and Control of Static Converters for Hybrid Storage Systems Fekik, Arezki, Benamrouche, Nacereddine, 2021-09-17 The energy transition initiated in recent years has enabled the growing integration of renewable production into the energy mix. Microgrids make it possible to maximize the efficiency of energy transmission from source to consumer by bringing the latter together geographically and by reducing losses linked to transport. However, the lack of inertia and the micro-grid support system makes it weak, and energy storage is necessary to ensure its proper functioning. Current storage technologies do not make it possible to provide both a large capacity of energy and power at the same time. Hybrid storage is a solution that combines the advantages of several technologies and reduces their disadvantages. Modeling and Control of Static Converters for Hybrid Storage Systems covers the modeling, control theorems, and optimization techniques that solve many scientific problems for researchers in the field of power converter control for renewable energy hybrid storage and places particular emphasis on the modeling and control of static converters for hybrid storage systems. Covering topics ranging from energy storage to power generation, this book is ideal for automation engineers, electrical engineers, mechanical engineers, professionals, scientists, academicians, master's and doctoral students, and researchers in the disciplines of electrical and mechanical engineering. |
| data science for wind energy: Meteorology for Wind Energy Lars Landberg, 2015-12-14 Most practitioners within wind energy have only a very basic knowledge about meteorology, leading to a lack of understanding of one of the most fundamental subjects in wind energy. This book will therefore provide an easy-to-understand introduction to the subject of meteorology, as seen from the viewpoint of wind energy. Catering for a range of academic backgrounds, the book is mathematically rigorous with accessible explanations for non-mathematically oriented readers. Through exercises in the text and at the end of each chapter the reader will be challenged to think, seek further information and practice the knowledge obtained from reading the book. This practical yet comprehensive reference will enable readers to fully understand the theoretical background of meteorology with wind energy in mind and will include topics such as: measurements; wind profiles; wakes; modelling; turbulence and the fundamentals of atmospheric flow on all scales including the local scale. Key features: • Provides practitioners of wind energy with a solid theoretical grounding in relevant aspects of meteorology enabling them to exercise useful judgment in matters related to resource estimation, wind farm development, planning, turbine design and electrical grids. • Supports a growing area of professional development with the increasing importance of wind energy estimation in all aspects of electrical energy production from wind. • Accompanying website includes data sets for exercises in data analysis, photographs, animations & worked examples, helping to further bridge the gap between theory and practice. Meteorology for Wind Energy: An Introduction is aimed at engineers, developers and project managers in the wind power and electrical utility sectors without the essential theoretical background required to understand the topic. It will also have significant appeal to senior undergraduate and postgraduate students of Wind Energy, Environmental Studies or Renewables Studies. |
| data science for wind energy: Recent Developments in Statistics and Data Science Regina Bispo, Lígia Henriques-Rodrigues, Russell Alpizar-Jara, Miguel de Carvalho, 2022-11-28 This volume presents a collection of twenty-five peer-reviewed articles carefully selected from the contributions presented at the XXV Congress of the Portuguese Statistical Society (2021). Containing state-of-the-art developments in theoretical and applied statistics, the book will be accessible to readers with a background in mathematics and statistics, but will also be of interest to researchers from other scientific disciplines (e.g., biology, economics, medicine), who will find a broad range of relevant applications. |
| data science for wind energy: Advances in Data Science Leman Akoglu, Emilio Ferrara, Mallayya Deivamani, Ricardo Baeza-Yates, Palanisamy Yogesh, 2018-11-28 This book constitutes the thoroughly refereed post-conference proceedings of the Third International Conference on Advances in Data Science, ICIIT 2018, held in Chennai, India, in December 2018. The 11 full papers along with 4 short papers presented were carefully reviewed and selected from 74 submissions.The papers are organized in topical sections on data science foundations, data management and processing technologies, data analytics and its applications. |
| data science for wind energy: Artificial Intelligence for Renewable Energy Systems Ajay Kumar Vyas, S. Balamurugan, Kamal Kant Hiran, Harsh S. Dhiman, 2022-03-02 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. |
| data science for wind energy: Handbook of Wind Power Systems Panos M. Pardalos, Steffen Rebennack, Mario V. F. Pereira, Niko A. Iliadis, Vijay Pappu, 2014-01-15 Wind power is currently considered as the fastest growing energy resource in the world. Technological advances and government subsidies have contributed in the rapid rise of Wind power systems. The Handbook on Wind Power Systems provides an overview on several aspects of wind power systems and is divided into four sections: optimization problems in wind power generation, grid integration of wind power systems, modeling, control and maintenance of wind facilities and innovative wind energy generation. The chapters are contributed by experts working on different aspects of wind energy generation and conversion. |
| data science for wind energy: Data Science Beiji Zou, Min Li, Hongzhi Wang, Xianhua Song, Wei Xie, Zeguang Lu, 2017-09-15 This two volume set (CCIS 727 and 728) constitutes the refereed proceedings of the Third International Conference of Pioneering Computer Scientists, Engineers and Educators, ICPCSEE 2017 (originally ICYCSEE) held in Changsha, China, in September 2017. The 112 revised full papers presented in these two volumes were carefully reviewed and selected from 987 submissions. The papers cover a wide range of topics related to Basic Theory and Techniques for Data Science including Mathematical Issues in Data Science, Computational Theory for Data Science, Big Data Management and Applications, Data Quality and Data Preparation, Evaluation and Measurement in Data Science, Data Visualization, Big Data Mining and Knowledge Management, Infrastructure for Data Science, Machine Learning for Data Science, Data Security and Privacy, Applications of Data Science, Case Study of Data Science, Multimedia Data Management and Analysis, Data-driven Scientific Research, Data-driven Bioinformatics, Data-driven Healthcare, Data-driven Management, Data-driven eGovernment, Data-driven Smart City/Planet, Data Marketing and Economics, Social Media and Recommendation Systems, Data-driven Security, Data-driven Business Model Innovation, Social and/or organizational impacts of Data Science. |
| data science for wind energy: Airborne Wind Energy Roland Schmehl, 2018-03-31 This book provides in-depth coverage of the latest research and development activities concerning innovative wind energy technologies intended to replace fossil fuels on an economical basis. A characteristic feature of the various conversion concepts discussed is the use of tethered flying devices to substantially reduce the material consumption per installed unit and to access wind energy at higher altitudes, where the wind is more consistent. The introductory chapter describes the emergence and economic dimension of airborne wind energy. Focusing on “Fundamentals, Modeling & Simulation”, Part I includes six contributions that describe quasi-steady as well as dynamic models and simulations of airborne wind energy systems or individual components. Shifting the spotlight to “Control, Optimization & Flight State Measurement”, Part II combines one chapter on measurement techniques with five chapters on control of kite and ground stations, and two chapters on optimization. Part III on “Concept Design & Analysis” includes three chapters that present and analyze novel harvesting concepts as well as two chapters on system component design. Part IV, which centers on “Implemented Concepts”, presents five chapters on established system concepts and one chapter about a subsystem for automatic launching and landing of kites. In closing, Part V focuses with four chapters on “Technology Deployment” related to market and financing strategies, as well as on regulation and the environment. The book builds on the success of the first volume “Airborne Wind Energy” (Springer, 2013), and offers a self-contained reference guide for researchers, scientists, professionals and students. The respective chapters were contributed by a broad variety of authors: academics, practicing engineers and inventors, all of whom are experts in their respective fields. |
| data science for wind energy: Offshore Wind Energy Technology Olimpo Anaya-Lara, John Olav Tande, Kjetil Uhlen, Karl Merz, 2018-05-11 A COMPREHENSIVE REFERENCE TO THE MOST RECENT ADVANCEMENTS IN OFFSHORE WIND TECHNOLOGY Offshore Wind Energy Technology offers a reference based on the research material developed by the acclaimed Norwegian Research Centre for Offshore Wind Technology (NOWITECH) and material developed by the expert authors over the last 20 years. This comprehensive text covers critical topics such as wind energy conversion systems technology, control systems, grid connection and system integration, and novel structures including bottom-fixed and floating. The text also reviews the most current operation and maintenance strategies as well as technologies and design tools for novel offshore wind energy concepts. The text contains a wealth of mathematical derivations, tables, graphs, worked examples, and illustrative case studies. Authoritative and accessible, Offshore Wind Energy Technology: Contains coverage of electricity markets for offshore wind energy and then discusses the challenges posed by the cost and limited opportunities Discusses novel offshore wind turbine structures and floaters Features an analysis of the stochastic dynamics of offshore/marine structures Describes the logistics of planning, designing, building, and connecting an offshore wind farm Written for students and professionals in the field, Offshore Wind Energy Technology is a definitive resource that reviews all facets of offshore wind energy technology and grid connection. |
| data science for wind 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 for wind energy: The Economics of Wind Energy , 2009 |
Data Science for Wind Energy - aml.engr.tamu.edu
e orts have been made to publish reliable data and information, but the author and publisher cannot assume responsibility for the validity of all materials or the consequences of their use. e …
DSWE: Data Science for Wind Energy - The Comprehensive …
Description Data science methods used in wind energy applications. Current functionalities include creating a multi-dimensional power curve model, performing power curve function …
A collection and categorization of open-source wind and wind …
datasets that can be used for wind power forecasting. We show that there are pub- source datasets and compare their methods on them. Over the past few years, the absolute and …
Data Analytics Methods for Wind Energy Applications
Almost invariably does the wind indus-try resort to data driven methods for a solution, namely that wind data and the corresponding turbine response data (bending mo-ments or power outputs) …
Data Science for Wind Energy, by Yu Ding - Texas A&M …
overview of wind energy, its importance and issues in predicting its output energy due to many sources of uncertainties. The following chapters provide forecasting models which can be …
An overview of wind-energy-production prediction bias, …
Herein, we present a literature review of the energy yield assessment errors across the global wind energy industry. We identify a long-term trend of reduction in the overprediction bias, …
Data-driven Arctic wind energy analysis by statistical and
achieved the target goal of developing data-driven Arctic wind energy analysis by statistical and learning approaches for wind parks and turbines in the Norwegian Arctic area.
PREDICTIVE MAINTENANCE FOR WIND TURBINES …
Worldwide, there is an installed wind power capacity of more than 600 GW. Annual additions around the world comprised 50 GW and above over the past five years. The trend shows that …
Harnessing Big Data and Data Science Across Energy Sectors
Over the last decade, the availability of data about energy systems has surged, and in parallel advances in machine learning techniques to analyze that data have been rapid. The …
Validation of wind resource and energy production …
Due to financial and temporal limitations, the small wind community relies upon simplified wind speed models and energy production simulation tools to assess site suitability and produce …
Data Science for Wind Energy - api.pageplace.de
Data Science for Wind Energy arrives at the right time, becoming one of the rst dedicated volumes to bridge the gap, and provides expositions of relevant data science methods and …
Intelligent digital twin machine learning system for real-time …
Current DT research for wind turbine power generation has focused on providing wind speed and power generation predictions reliant on Supervisory Control and Data Acquisition (SCADA) …
Grand challenges in the science of wind energy
Wind en-ergy is already playing a role as a mainstream source of electricity, driven by decades of sci-entific discovery and technology development. Additional research and exploration of …
Using Available Data and Information to Identify Offshore …
We have developed a spatial model that evaluates the potential impacts of offshore wind energy development on wildlife, habitats and human uses of the ocean. The model is evaluated for …
Renewable Energy Data, Analysis, and Decisions: A Guide …
This guide describes data requirements for making various renewable energy decisions and discusses the tools and analyses necessary to transform these data into recommendations for …
Offshore Resource Assessment and Design Conditions: A …
The dual purposes of this document are (1) to provide an initial overview of the information required by a range of stakeholders to effectively deploy MHK and wind energy systems …
Data Mining For Wind Energy Site Selection
Abstract—The objective of this paper is to analyze suitability of a particular site for wind turbine installation.
Evaluation of wind speed estimates in reanalyses for wind …
Employing metrics such as bias, RMSE and correlation, we evaluate the performance of the reanalyses with respect to (a) the local surface characteristics (offshore, flat onshore, hilly …
The Power Curve Working Group’s Assessment of Wind …
To search for the optimal power-curve modeling method, the PCWG launched the third iteration of its intelligence-sharing (Share-3) exercise. In 2018, we collected and analyzed 55 data sets of …
Wind resource assessment using LiDAR - UiT
Statistical evaluations and Weibull distribution analyses support the reliability of LiDAR data for wind energy estimation. Future research directions include validating LiDAR performance over …
A near-real-time data-assimilative model of the solar corona
6 days ago · The solar magnetic field provides the energy that drives solar flares and coronal mass ejections (CMEs) (2), determines the structure of the corona and solar wind (3), and …
Offshore Wind Supply Chain & Workforce Opportunity …
• Section 3 - Assessment of the Maine supply chain with additional focus on OSW vessels, data science and AI and Disadvantaged Business Enterprises. • Section 4 – An opportunity analysis …
DSWE: Data Science for Wind Energy - The Comprehensive …
Title Data Science for Wind Energy Version 1.8.2 Description Data science methods used in wind energy applications. Current functionalities include creating a multi-dimensional power curve …
WIND ENERGY RESEARCH & DEVELOPMENT - nrel.gov
Leverages global perspectives and research to lower the levelized cost of energy, facilitate wind energy deployment through environmental compatibility, and fosters collaborative research and …
Offshore Wind Energy Science Updates - National Oceanic …
Offshore Wind Energy Science Updates. Andy Lipsky, Elizabeth Methratta, Chris Orphanides, Angela Silva, and Jon Hare. ... Determine effects of wind development on survey data, stock …
Wind Turbine Lab Report - cpb-us-w2.wpmucdn.com
Nov 8, 2019 · fossil fuels consumption, wind energy has been frequently mentioned, for it essentially comes from the sun, thus inexhaustible. Through wind, turbines rotate and convert …
An Overview of Wind Energy Production Prediction Bias, …
AEP percent. For clarity, the grey horizontal lines separate data from each subcategory. Fig. 3: Ranges of energy production uncertainties in different categories and subcategories, according …
2020 Wind Energy Research and Development Highlights
enables existing wind energy facilities to improve productivity and increase profits. For more information on the new model, see the FLORIS 1.1.14 documentation or read the new …
The Atmospheric Grand Challenges in the Science of Wind …
control of fleets of wind plants working synergistically within the electricity grid. Addressing these challenges could enable wind power to provide as much as half of our global electricity needs …
An Integrated Assessment of China’s Wind Energy Potential
leads the world in installed capacity of renewable energy and in wind generation capacity (REN21, 2012). Growth in wind generation surpassed growth in coal-fired electricity for the first time ever …
TEXAS 4-H 4-H District-Wide STEM Research Project
Main Question: Is the energy produced by a wind turbine different when the blade angles and the wind power are changed? Independent Variable : Fan distance (30 cm or 50 cm), blade angles …
Offshore Resource Assessment and Design Conditions: A …
range of stakeholders to effectively deploy MHK and wind energy systems offshore and (2) to identify ... data for evaluating the energy potential, economic viability, and engineering …
Iran’s Transition to Wind Energy - Shahroodut.ac.ir
Keywords: Energy transition, Iran’s renewable energy, Wind energy science, Wind energy engineering, Wind energy policy, Energy diplomacy, Future of energy, Renewable energy’s …
Research Projects in Renewable Energy for High School Student
these areas of renewable energy: biofuels, wind, and solar. Science projects described here apply the disciplines of chemistry, physics, biology, and mathematics. ... wind energy, or solar …
Wind PowerWind Power Fundamentals - MIT
Jan 24, 2009 · US federal policy for wind energy – Periodic expp(),iration of Production Tax Credit (PTC) in 1999, 2001, and 2003 – 2009 Stimulus package is supportive of wind power – Energy …
Assessing the Wind Energy Potential in Bangladesh - NREL
Enabling Wind Energy Development with Data Products Mark Jacobson, Caroline Draxl, Tony Jimenez, and Barbara O’Neill National Renewable Energy Laboratory Taj Capozzola ...
Satellite Data Applications for Sustainable Energy Transitions
introduction, we discuss satellite data applications in energy supply, energy demand, energy impacts, and energy resilience. We then describe an example of a satellite data distribution …
Enabling the SMART Wind Power Plant of the Future …
interacts with that flow. Though the wind industry and wind energy technology have advanced dramatically in recent decades, uncertainty in the science around wind plant physics threatens …
Wind Turbine Gearbox Condition Monitoring Round Robin …
Dec 31, 2004 · The National Renewable Energy Laboratory’s (NREL) contributions to this report were funded by the Wind and Water Power Program, Office of Energy Efficiency and …
Renewable Energy in China - NREL
Ministry of Science & Technology (MOST) ... tenance of wind measurement systems and data collection standards and codes. In addition, the project is gathering on- ... Fact sheet describes …
DEVELOPING THE Offshore Wind - media.audubon.org
1.2 The Importance of Renewable Energy 9 1.3 The Role for Offshore Wind and Addressing Unintended Consequences 9 1.4 Continuing to Protect Shorebirds & Seabirds 11 1.5 Collective …
Program Area Presentation - Department of Energy
• Lidar Buoy Deployments & Science & - Developed and deployed wind buoys for MBL data collection to improve forecasting modeling in eastern and western costal offshore wind …
Delaware Sea Grant Data Activity Understanding downwind …
Delaware Sea Grant Data Activity . Understanding downwind effects. Summary . Since the first wind-powered generator was invented in 1888, humans have been creating and installing more …
Twin Groves Wind Energy Facility Cut-in Speeds
Bat fatalities at wind energy facilities received little attention in North America until high numbers of fatalities were reported in the eastern U.S. (Johnson 2005, Arnett 2005, Arnett et al. 2008). …
Short-term wind forecasting using statistical models with a
there is precedent to use synthetic data for wind speed forecasting. 4. The NREL WIND Toolkit The synthetic data set used in this paper is NREL’s WIND Toolkit [4]. Based on the WRF …
Hourly energy demand generation and weather - ResearchGate
The renewable, hydroelectric, solar and wind, energy production was rarely not enough to ... This is the last step of any Data Science project and also the most important step and in
OPTIMIZING WIND ENERGY SYSTEMS USING MACHINE …
Computer Science & IT Research Journal, Volume 4, Issue 3, December 2023 Udo, Kwakye, Ekechukwu, & Ogundipe, P. 386-397 Page 390 instance, a time-series model can forecast …
Grand challenges in the science of wind energy
REVIEW RENEWABLE ENERGY Grand challenges in the science of wind energy Paul Veers 1*, Katherine Dykes2*, Eric Lantz *, Stephan Barth3, Carlo L. Bottasso4, Ola Carlson5, Andrew …
Wind for Schools Project Curriculum Brief - NREL
Title: Wind for Schools Project Curriculum Brief (Fact Sheet), Wind And Water Power Program (WWPP) Author: I. Baring-Goud: NREL Subject: The U.S. Department of Energy's (DOE's) …
Wind and Solar PV Resource Aggregation Study for South …
electricity from wind and solar PV energy was determined based on data sets for five years covering the whole country with a spatial resolution of 5 km by 5 km and a temporal resolution …
Offshore wind: An opportunity for cost-competitive ... - Science
nomics, and the utilization of onshore wind power (1–5, 17), little attention has been devoted to the possibility of using offshore wind resources to decarbonize coastal energy systems. Lu et …
An Operations and Maintenance Roadmap for U.S. Offshore …
• Low data processing efficiency, lack of standardization, and lack of confidence in models developed using data: With many sensors and a large amount of data from offshore wind …
A Systematic Framework for Projecting the Future Cost of …
wind energy technology costs have declined significantly in recent years, and many forecasts indicate that these cost reductions will continue for the foreseeable future. Between 2014 and …
2022 Cost of Wind Energy Review - NREL
understanding the variability in wind energy LCOE across the country. • The primary elements of this 2022analysis include: − Estimated LCOE for (1) a representative . land-based wind . …
Solar and wind power data from the Chinese State Grid …
consisting of data collected from on-site renewable energy stations, including six wind farms and eight solar stations in China, is provided. Over two years (2019–2020), power generation and ...
Analysis of the Use of Wind Technical Report - NREL
Additionally, a new primary community/science facility at South Pole Station is under construction; thus only estimates of its expected power consumption are available. This ... Wind Speed Data …
WIND ENERGY RESEARCH & DEVELOPMENT Atmospheric …
WIND ENERGY RESEARCH & DEVELOPMENT Atmospheric Science Author: Caroline Draxl, the U.S. Department of Energy's National Renewable Energy Laboratory Subject: Overview of …
Quantile Combination for the EEM20 Wind Power Forecasting …
historical wind energy production training data set. Wind power forecasting has developed in parallel with the wider wind industry driven by the needs of market participants and power …
Evaluation of Global Reanalysis Land Surface Wind Speed …
observed wind data. We investigated both overall trend and piecewise trends over land areas in recent decades. The moti-vation of this work is that closer agreement of reanalysis products …
Exploring Renewable Energy Opportunities in Select …
Jun 29, 2020 · developers, and other actors in the ASEAN member states assess the cost of renewable-energy-based, utility-scale, land-based wind and solar PV opportunities and to: 1. …
Proceedings from the State of the Science and Technology …
The U.S. Department of Energy Wind Energy Technologies Office, and the National Renewable Energy Laboratory convened a workshop entitled the State of the Science and Technology for …
WIND ENERGY RESEARCH & DEVELOPMENT - explore.re …
Disseminating wind energy information and providing access to wind energy tools, maps, and other resources. Outreach and Engagement for Community Impact Issues. Researching local …
PENNSYLVANIA STATE UNIVERSITY WIND ENERGY CLUB
Although there are no quantitative trends between the data, a quali tative look through the social media feed ... cused on basic climate science, wind energy myth busting, and careers in …
Wind resource assessment and energy potential of selected …
1 . Wind resource assessment and energy potential of selected locations1 in Fiji 2 Kunal K. 1Dayal1*, John E. Cater2, Michael J. Kingan, Gilles D. Bellon3, and Rajnish N. Sharma13 4 1 …
Diagnostic Models for Wind Turbine Gearbox Components …
(DOE) is to promote the adoption of clean energy sources. Wind energy will play a big role in this energy revolution. DOE has published scenarios that predict 20% of U.S. energy needs coming …
Enabling Wind Power Nationwide Jose Zayas, Director
Wind Energy Technologies Office 12 eere.energy.gov Wind Energy Technologies Office Total Funding, FY 2014 – FY 2016 Congressional Appropriations by Subprogram FY 2014 – FY 2016 …
Generative AI for Energy Harvesting Internet of Things …
2) Wind Energy: Wind energy harvesting typically employs wind turbines to convert the kinetic energy of wind into electricity, which is particularly suitable for areas with abun-dant wind …
Lecture # 22 Wind Energy - MIT OpenCourseWare
Electrification Worldwide • Less developed countries have 80%of world s population,consume ~ 30% of total energy • ~2Bpeople withoutconsistent access to electricity
2020 Cost of Wind Energy Review - NREL
Science and Technology (contractor to WETO) for reviewing prior versions of this manuscript. ... (2020) because the market data for distributed wind energy projects in 2020 only comprised …
Offshore Wind and Fisheries: The Science Behind …
Offshore Wind and Fisheries: The Science Behind Coexistence . Climate change is a serious threat to the marine . environment, fish, and fisheries. 1. Offshore wind farms provide an …
Wind Power Fundamentals - MIT OpenCourseWare
Reported in US DOE. 2008 Renewable Energy Data Book. Policy Support Historically US federal policy for wind energy – Periodic expiration of Production Tax Credit (PTC) in 1999, ...
