Data Science for Wind Energy

By: Yu Ding

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Paperback | 18 December 2020 | Edition Number 1

At a Glance

Paperback
426 Pages

Dimensions(cm)
23.4 x 15.6 x 2.7

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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

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.

Industry Reviews

"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

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