Description
| Product ID: | 9780367729097 |
| Product Form: | Paperback / softback |
| Country of Manufacture: | GB |
| Title: | Data Science for Wind Energy |
| Authors: | Author: Yu Ding |
| Page Count: | 424 |
| Subjects: | Probability and statistics, Probability & statistics, Alternative and renewable energy sources and technology, Environmental science, engineering and technology, Databases, Artificial intelligence, Alternative & renewable energy sources & technology, Environmental science, engineering & technology, Databases, Artificial intelligence |
| Description: | This book shows how data science methods can improve decision making for wind energy applications. A broad set of data science methods will be covered, and the data science methods will be described in the context of wind energy applications, with specific wind energy examples and case studies. 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
The Open Access version of this book, available at http://www.taylorfrancis.com, has been made available under a Creative Commons (CC) 4.0 license. |
| Imprint Name: | Chapman & Hall/CRC |
| Publisher Name: | Taylor & Francis Ltd |
| Country of Publication: | GB |
| Publishing Date: | 2020-12-18 |