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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...

Ausführliche Beschreibung

Gespeichert in:
1. Verfasser: Ding, Yu
Format: Online
Sprache:Englisch
Veröffentlicht: Taylor & Francis 2025
Schlagworte:
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
Online-Zugang:ONIX_20250512_9780429956515_7
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