Explore
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
This book is included in DOAB.
Why read this book? Have your say.
You must be logged in to comment.
Rights Information
Are you the author or publisher of this work? If so, you can claim it as yours by registering as an Unglue.it rights holder.Downloads
This work has been downloaded 6 times via unglue.it ebook links.
- 6 - pdf (CC BY-NC-ND) at Unglue.it.
Keywords
- Alternative & renewable energy sources & technology
- artificial intelligence
- Computer science
- Computing & information technology
- Databases
- Energy technology & engineering
- Environmental science, engineering & technology
- Mathematics
- Mathematics & science
- Probability & statistics
- Technology, engineering, agriculture
- thema EDItEUR::P Mathematics and Science::PB Mathematics::PBT Probability and statistics
- thema EDItEUR::U Computing and Information Technology::UN Databases
- thema EDItEUR::U Computing and Information Technology::UY Computer science::UYQ Artificial intelligence
Links
DOI: 10.1201/9780429490972Editions