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Data Science for Wind Energy

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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 optim...

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Détails bibliographiques
Auteur principal: Ding, Yu (auth)
Format: Électronique Chapitre de livre
Langue:anglais
Publié: Taylor & Francis 2020
Sujets:
Artificial intelligence
Databases
Environmental science, engineering & technology
Alternative & renewable energy sources & technology
Probability & statistics
Bayesian Additive Regression Trees;SVM Model;data mining;Power Curve Model;data analytics;Wind Speed;renewable energy;GEV Distribution;wind turbines;PACF Plot;machine learning;Wind Turbine;bayesian methods;Binning Method;data science methods;Local Wind Field;wind energy applications;ARMA Model;turbine reliability assessment;Wind Field Analysis;near-ground wind field analysis;Ahead Forecast;Wind Speed Forecast;Power Curve;Wind Farm;Test Turbine;Importance Sampling Density;Be;GMRF Model
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