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...

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書誌詳細
第一著者:	Ding, Yu
フォーマット:	Online
言語:	英語
出版事項:	Taylor & Francis 2025
主題:	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
オンライン･アクセス:	ONIX_20250512_9780429956515_7
タグ:	タグ追加タグなし, このレコードへの初めてのタグを付けませんか!

Data Science for Wind Energy

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