Data Science for Wind Energy

• Hoofdauteur: Ding, Yu (auth)
• Formaat: Elektronisch Hoofdstuk
• Taal: Engels
• Gepubliceerd in: Taylor & Francis 2020
• Onderwerpen: 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
• Online toegang: DOAB: download the publication; DOAB: description of the publication