Ding, Yu (Electrical and Computer Engineer),
Data science for wind energy / Yu Ding. - 1 online resource : illustrations
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
9780429490972 0429490976 9780429956508 0429956509 9780429956515 0429956517
Wind power--Mathematical models.
Wind power--Data processing.
TECHNOLOGY & ENGINEERING / Mechanical
BUSINESS & ECONOMICS / Statistics
COMPUTERS / General
COMPUTERS / Computer Graphics / Game Programming & Design
TJ820 / .D56 2020
621.31/21360285 621.45
Data science for wind energy / Yu Ding. - 1 online resource : illustrations
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
9780429490972 0429490976 9780429956508 0429956509 9780429956515 0429956517
Wind power--Mathematical models.
Wind power--Data processing.
TECHNOLOGY & ENGINEERING / Mechanical
BUSINESS & ECONOMICS / Statistics
COMPUTERS / General
COMPUTERS / Computer Graphics / Game Programming & Design
TJ820 / .D56 2020
621.31/21360285 621.45
