TY  - BOOK
AU  - Ding,Yu
TI  - Data science for wind energy
SN  - 9780429490972
AV  - TJ820 .D56 2020
U1  - 621.31/21360285 23
PY  - 2020///]
CY  - Boca Raton
PB  - CRC Press
KW  - Wind power
KW  - Mathematical models
KW  - Data processing
KW  - TECHNOLOGY & ENGINEERING / Mechanical
KW  - bisacsh
KW  - BUSINESS & ECONOMICS / Statistics
KW  - COMPUTERS / General
KW  - COMPUTERS / Computer Graphics / Game Programming & Design
N2  - 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. 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
UR  - https://www.taylorfrancis.com/books/9780429490972
UR  - http://www.oclc.org/content/dam/oclc/forms/terms/vbrl-201703.pdf
ER  -