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OverviewData 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 Full Product DetailsAuthor: Yu Ding (Texas A&M University, USA)Publisher: Taylor & Francis Ltd Imprint: CRC Press Weight: 0.725kg ISBN: 9781138590526ISBN 10: 1138590525 Pages: 400 Publication Date: 24 May 2019 Audience: College/higher education , General/trade , Tertiary & Higher Education , General Format: Hardback Publisher's Status: Active Availability: In Print  This item will be ordered in for you from one of our suppliers. Upon receipt, we will promptly dispatch it out to you. For in store availability, please contact us. Table of ContentsChapter 1 □ Introduction Part I Wind Field Analysis Chapter 2 □ A Single Time Series Model Chapter 3 □ Spatiotemporal Chapter 4 □ Regimeswitching Part II Wind Turbine Performance Analysis Chapter 5 □ Power Curve Modeling and Analysis Chapter 6 □ Production Efficiency Analysis Chapter 7 □ Quantification of Turbine Upgrade Chapter 8 □ Wake Effect Analysis Chapter 9 □ Overview of Turbine Maintenance Optimization Chapter 10 □ Extreme Load Analysis Chapter 11 □ Computer Simulator Based Load Analysis Chapter 12 □ Anomaly Detection and Fault DiagnosisReviewsThis is the first book that focuses on the data science methodologies and their applications in a growing field, wind energy. It is well-organized and well-written. It will enhance the knowledge base of data science and its applications in the wind energy field. -- Elsayed A. Elsayed, Professor, Rutgers University This is the first book that focuses on the data science methodologies and their applications in a growing field, wind energy. It is well-organized and well-written. It will enhance the knowledge base of data science and its applications in the wind energy field. -- Elsayed A. Elsayed, Professor, Rutgers University Author InformationYu Ding is the Mike and Sugar Barnes Professor of Industrial and Systems Engineering and Professor of Electrical and Computer Engineering at Texas A&M University, and a Fellow of the Institute of Industrial & Systems Engineers and the American Society of Mechanical Engineers Tab Content 6Author Website:Countries AvailableAll regions |
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