Data science for wind energy

425 Pages·2020·61.766 MB·English

by  Ding, Yu

Data Science for Wind
Energy

Data Science for Wind
Energy
Yu Ding
CRC Press
Taylor & Francis Group
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Version Date: 20190508
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To my parents.

Contents
Foreword xv
Preface xvii
Acknowledgments xxi
Chapter
1(cid:4) Introduction 1
1.1 WINDENERGYBACKGROUND 2
1.2 ORGANIZATIONOFTHISBOOK 6
1.2.1 Who Should Use This Book 8
1.2.2 Note for Instructors 9
1.2.3 Datasets Used in the Book 9
Part I Wind Field Analysis
Chapter
2(cid:4) A Single Time Series Model 17
2.1 TIMESCALEINSHORT-TERMFORECASTING 18
2.2 SIMPLEFORECASTINGMODELS 19
2.2.1 Forecasting Based on Persistence Model 19
2.2.2 Weibull Distribution 19
2.2.3 Estimation of Parameters in Weibull Distribution 20
2.2.4 Goodness of Fit 21
2.2.5 Forecasting Based on Weibull Distribution 23
2.3 DATATRANSFORMATIONANDSTANDARDIZATION 24
2.4 AUTOREGRESSIVEMOVINGAVERAGEMODELS 27
2.4.1 Parameter Estimation 28
2.4.2 Decide Model Order 29
2.4.3 Model Diagnostics 31
vii
viii (cid:4) Contents
2.4.4 Forecasting Based on ARMA Model 34
2.5 OTHERMETHODS 38
2.5.1 Kalman Filter 38
2.5.2 Support Vector Machine 40
2.5.3 Artificial Neural Network 45
2.6 PERFORMANCEMETRICS 48
2.7 COMPARINGWINDFORECASTINGMETHODS 50
Chapter
3(cid:4) Spatio-temporal Models 57
3.1 COVARIANCEFUNCTIONSANDKRIGING 57
3.1.1 Properties of Covariance Functions 58
3.1.2 Power Exponential Covariance Function 59
3.1.3 Kriging 60
3.2 SPATIO-TEMPORALAUTOREGRESSIVEMODELS 65
3.2.1 Gaussian Spatio-temporal Autoregressive Model 65
3.2.2 Informative Neighborhood 68
3.2.3 Forecasting and Comparison 69
3.3 SPATIO-TEMPORALASYMMETRYANDSEPARABILITY 73
3.3.1 Definition and Quantification 73
3.3.2 Asymmetry of Local Wind Field 74
3.3.3 Asymmetry Quantification 76
3.3.4 Asymmetry and Wake Effect 78
3.4 ASYMMETRICSPATIO-TEMPORALMODELS 79
3.4.1 AsymmetricNon-separableSpatio-temporalModel 79
3.4.2 Separable Spatio-temporal Models 81
3.4.3 Forecasting Using Spatio-temporal Model 81
3.4.4 Hybrid of Asymmetric Model and SVM 83
3.5 CASESTUDY 83
Chapter
4(cid:4) Regime-switching Methods for Forecasting 93
4.1 REGIME-SWITCHINGAUTOREGRESSIVEMODEL 93
4.1.1 Physically Motivated Regime Definition 94
4.1.2 Data-driven Regime Determination 96
4.1.3 Smooth Transition between Regimes 97
Contents (cid:4) ix
4.1.4 Markov Switching between Regimes 98
4.2 REGIME-SWITCHINGSPACE-TIMEMODEL 99
4.3 CALIBRATIONINREGIME-SWITCHINGMETHOD 104
4.3.1 Observed Regime Changes 105
4.3.2 Unobserved Regime Changes 106
4.3.3 Framework of Calibrated Regime-switching 107
4.3.4 Implementation Procedure 111
4.4 CASESTUDY 113
4.4.1 Modeling Choices and Practical Considerations 113
4.4.2 Forecasting Results 115
Part II Wind Turbine Performance Analysis
Chapter
5(cid:4) Power Curve Modeling and Analysis 125
5.1 IECBINNING:SINGLE-DIMENSIONALPOWERCURVE 126
5.2 KERNEL-BASEDMULTI-DIMENSIONALPOWERCURVE 127
5.2.1 Need for Nonparametric Modeling Approach 128
5.2.2 Kernel Regression and Kernel Density Estimation131
5.2.3 Additive Multiplicative Kernel Model 134
5.2.4 Bandwidth Selection 136
5.3 OTHERDATASCIENCEMETHODS 137
5.3.1 k-Nearest Neighborhood Regression 138
5.3.2 Tree-based Regression 139
5.3.3 Spline-based Regression 143
5.4 CASESTUDY 145
5.4.1 Model Parameter Estimation 145
5.4.2 ImportantEnvironmentalFactorsAffectingPower
Output 147
5.4.3 Estimation Accuracy of Different Models 150
Chapter
6(cid:4) Production Efficiency Analysis and Power Curve 159
6.1 THREEEFFICIENCYMETRICS 159
6.1.1 Availability 160
6.1.2 Power Generation Ratio 160
6.1.3 Power Coefficient 161