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Data science for wind energy

By: Ding, Yu.
Material type: materialTypeLabelBookPublisher: Boca Raton CRC Press 2019Description: xxii, 400 p. Includes bibliographical references and index.ISBN: 9781138590526.Subject(s): Wind power - Data processing | Wind power - Mathematical models | Game programming - Design | Kernel regression | Bayesian inferenceDDC classification: 621.45 Summary: 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. 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 https://www.crcpress.com/Data-Science-for-Wind-Energy/Ding/p/book/9781138590526
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Slot 1758 (2 Floor, East Wing) Non-fiction 621.45 D4D2 (Browse shelf) Available 200243

Table of contents:

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

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.

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

https://www.crcpress.com/Data-Science-for-Wind-Energy/Ding/p/book/9781138590526

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