Data Science for Wind Energy
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 optim...
Сохранить в:
| Главный автор: | |
|---|---|
| Формат: | Online |
| Язык: | английский |
| Опубликовано: |
Taylor & Francis
2025
|
| Предметы: | |
| Online-ссылка: | ONIX_20250512_9780429956515_7 |
| Метки: |
Нет меток, Требуется 1-ая метка записи!
|
| _version_ | 1869525965756956672 |
|---|---|
| author | Ding, Yu |
| author_browse | Ding, Yu |
| author_facet | Ding, Yu |
| author_sort | Ding, Yu |
| collection | Directory of Open Access Books |
| description | 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 |
| format | Online |
| id | doab-20.500.12854ir-159267 |
| institution | Directory of Open Access Books |
| language | eng |
| publishDate | 2025 |
| publishDateRange | 2025 |
| publishDateSort | 2025 |
| publisher | Taylor & Francis |
| publisherStr | Taylor & Francis |
| record_format | ojs |
| spelling | doab-20.500.12854ir-1592672025-05-16T05:50:14Z Data Science for Wind Energy Ding, Yu Bayesian Additive Regression Trees SVM Model Power Curve Model Wind Speed GEV Distribution PACF Plot Wind Turbine Binning Method Local Wind Field ARMA Model Wind Field Analysis Ahead Forecast Wind Speed Forecast Power Curve Wind Farm Data Science Methods Test Turbine Importance Sampling Density Be GMRF Model Computer Simulators CMC Power Coefficient Importance Sampling Method Posterior Predictive Distribution thema EDItEUR::U Computing and Information Technology::UN Databases::UNF Data mining thema EDItEUR::T Technology, Engineering, Agriculture, Industrial processes::TH Energy technology and engineering::THV Alternative and renewable energy sources and technology thema EDItEUR::U Computing and Information Technology::UY Computer science::UYQ Artificial intelligence thema EDItEUR::P Mathematics and Science::PB Mathematics::PBT Probability and statistics 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 2025-05-13T04:15:52Z 2025-05-13T04:15:52Z 2025-05-12T09:31:54Z 2019 book ONIX_20250512_9780429956515_7 https://library.oapen.org/handle/20.500.12657/101466 9780429956515 9781138590526 9780429956492 9780429956508 9780367729097 9780429490972 https://directory.doabooks.org/handle/20.500.12854/159267 eng open access image/jpeg Attribution-NonCommercial-NoDerivatives 4.0 International https://library.oapen.org/bitstream/20.500.12657/101466/1/9780429956515.pdf Taylor & Francis Chapman and Hall/CRC 10.1201/9780429490972 10.1201/9780429490972 fa69b019-f4ee-4979-8d42-c6b6c476b5f0 Georgia Institute of Technology e9f4faa3-9aac-40dd-b63b-aec2d8ab48ad 9780429956515 9781138590526 9780429956492 9780429956508 9780367729097 9780429490972 Chapman and Hall/CRC 424 [...] open access |
| spellingShingle | Bayesian Additive Regression Trees SVM Model Power Curve Model Wind Speed GEV Distribution PACF Plot Wind Turbine Binning Method Local Wind Field ARMA Model Wind Field Analysis Ahead Forecast Wind Speed Forecast Power Curve Wind Farm Data Science Methods Test Turbine Importance Sampling Density Be GMRF Model Computer Simulators CMC Power Coefficient Importance Sampling Method Posterior Predictive Distribution thema EDItEUR::U Computing and Information Technology::UN Databases::UNF Data mining thema EDItEUR::T Technology, Engineering, Agriculture, Industrial processes::TH Energy technology and engineering::THV Alternative and renewable energy sources and technology thema EDItEUR::U Computing and Information Technology::UY Computer science::UYQ Artificial intelligence thema EDItEUR::P Mathematics and Science::PB Mathematics::PBT Probability and statistics Ding, Yu Data Science for Wind Energy |
| title | Data Science for Wind Energy |
| title_full | Data Science for Wind Energy |
| title_fullStr | Data Science for Wind Energy |
| title_full_unstemmed | Data Science for Wind Energy |
| title_short | Data Science for Wind Energy |
| title_sort | data science for wind energy |
| topic | Bayesian Additive Regression Trees SVM Model Power Curve Model Wind Speed GEV Distribution PACF Plot Wind Turbine Binning Method Local Wind Field ARMA Model Wind Field Analysis Ahead Forecast Wind Speed Forecast Power Curve Wind Farm Data Science Methods Test Turbine Importance Sampling Density Be GMRF Model Computer Simulators CMC Power Coefficient Importance Sampling Method Posterior Predictive Distribution thema EDItEUR::U Computing and Information Technology::UN Databases::UNF Data mining thema EDItEUR::T Technology, Engineering, Agriculture, Industrial processes::TH Energy technology and engineering::THV Alternative and renewable energy sources and technology thema EDItEUR::U Computing and Information Technology::UY Computer science::UYQ Artificial intelligence thema EDItEUR::P Mathematics and Science::PB Mathematics::PBT Probability and statistics |
| topic_facet | Bayesian Additive Regression Trees SVM Model Power Curve Model Wind Speed GEV Distribution PACF Plot Wind Turbine Binning Method Local Wind Field ARMA Model Wind Field Analysis Ahead Forecast Wind Speed Forecast Power Curve Wind Farm Data Science Methods Test Turbine Importance Sampling Density Be GMRF Model Computer Simulators CMC Power Coefficient Importance Sampling Method Posterior Predictive Distribution thema EDItEUR::U Computing and Information Technology::UN Databases::UNF Data mining thema EDItEUR::T Technology, Engineering, Agriculture, Industrial processes::TH Energy technology and engineering::THV Alternative and renewable energy sources and technology thema EDItEUR::U Computing and Information Technology::UY Computer science::UYQ Artificial intelligence thema EDItEUR::P Mathematics and Science::PB Mathematics::PBT Probability and statistics |
| url | ONIX_20250512_9780429956515_7 |
| work_keys_str_mv | AT dingyu datascienceforwindenergy |