Data Science for Wind Energy

Von:

Ding, Yu [VerfasserIn]

Resource type: Ressourcentyp: Buch (Online)Buch (Online)Sprache: Nicht definiert Verlag: [Erscheinungsort nicht ermittelbar] : Taylor & Francis, 2020Beschreibung: 1 Online-Ressource (425 p.)ISBN:

9780429490972
9781138590526
9780429956492
9780429956508
9780367729097

Schlagwörter:

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-Ressourcen:

Volltext frei verfügbar

Zusammenfassung: 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 insightsPPN: PPN: 1885789130Package identifier: Produktsigel: ZDB-94-OAB

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Namensnennung - Nicht-kommerziell - Keine Bearbeitungen 4.0 International CC BY-NC-ND 4.0 cc:

https://creativecommons.org/licenses/by-nc-nd/4.0/

English