Data Science for Wind Energy

Data Science for Wind Energy
Author: Yu Ding
Publisher: CRC Press
Total Pages: 0
Release: 2020-12-18
Genre: Business & Economics
ISBN: 9780367729097

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

Wind Energy

Wind Energy
Author: Vaughn Nelson
Publisher: CRC Press
Total Pages: 320
Release: 2009-03-16
Genre: Science
ISBN: 1420075691

Due to the mounting demand for energy and increasing population of the world, switching from nonrenewable fossil fuels to other energy sources is not an option-it is a necessity. Focusing on a cost-effective option for the generation of electricity, Wind Energy: Renewable Energy and the Environment covers all facets of wind energy and wind turbines