Beeld op de omslag

Data Science for Wind Energy

Toon andere (1) versies

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...

Volledige beschrijving

Bewaard in:
Bibliografische gegevens
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:OAPEN Library: download the publication
OAPEN Library: description of the publication
Tags: Voeg label toe
Geen labels, Wees de eerste die dit record labelt!

Gelijkaardige items

Gelijkaardige items