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Data Science For Wind Energy - Yu Ding

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      Présentation Data Science For Wind Energy de Yu Ding Format Relié

       - Livre Technologie

      Livre Technologie - Yu Ding - 01/05/2019 - Relié - Langue : Anglais

      . .

    • Auteur(s) : Yu Ding
    • Editeur : Crc Press
    • Langue : Anglais
    • Parution : 01/05/2019
    • Format : Moyen, de 350g à 1kg
    • Nombre de pages : 400.0
    • ISBN : 9781138590526



    • Résumé :

      Chapter 1 ? Introduction

      Part I Wind Field Analysis

      Chapter 2 ? A Single Time Series Model

      Chapter 3 ? Spatiotemporal

      Chapter 4 ? Regimeswitching

      Part II Wind Turbine Performance Analysis

      Chapter 5 ? Power Curve Modeling and Analysis

      Chapter 6 ? Production Efficiency Analysis

      Chapter 7 ? Quantification of Turbine Upgrade

      Chapter 8 ? Wake Effect Analysis

      Chapter 9 ? Overview of Turbine Maintenance Optimization

      Chapter 10 ? Extreme Load Analysis

      Chapter 11 ? Computer Simulator Based Load Analysis

      Chapter 12 ? Anomaly Detection and Fault Diagnosis

      ...

      Biographie:

      Yu Ding is the Mike and Sugar Barnes Professor of Industrial and Systems Engineering and Professor of Electrical and Computer Engineering at Texas A&M University, and a Fellow of the Institute of Industrial & Systems Engineers and the American Society of Mechanical Engineers

      ...

      Sommaire:

      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 insights
      ...

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