Supervised Machine Learning in Wind Forecasting and Ramp Event Prediction

206 Pages·2020·9.172 MB·English

by  Harsh S. Dhiman, Dipankar Deb, Valentina Emilia Balas

SUPERVISED
MACHINE
LEARNING IN WIND
FORECASTING
AND RAMP EVENT
PREDICTION

Wind Energy Engineering
SUPERVISED
MACHINE
LEARNING IN WIND
FORECASTING
AND RAMP EVENT
PREDICTION
HARSHS.DHIMAN
DepartmentofElectricalEngineering
InstituteofInfrastructureTechnologyResearchandManagement
Ahmedabad,India
DIPANKARDEB
DepartmentofElectricalEngineering
InstituteofInfrastructureTechnologyResearchandManagement
Ahmedabad,India
VALENTINAEMILIABALAS
DepartmentofAutomationandAppliedInformatics
AurelVlaicuUniversityofArad
Arad,Romania
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Dedicated to almighty, my parents Renu and Sanjay, brother Hardik, and
the love of my life Shiwangi.
Harsh S. Dhiman
Dedicated to my wife Indulekha and my son Rishabh for providing me
unending love and support.
Dipankar Deb
Dedicated to my husband Marius for patience, love, friendship, and humor.
Valentina Emilia Balas
Contents
Listoffigures xi
Listoftables xv
Biography xvii
Preface xix
Acknowledgments xxi
Acronyms xxiii
1. Introduction 1
1.1. Renewableenergyfocus 1
1.2. Windenergy:issuesandchallenges 3
1.3. Machinelearninginalliedareasofwindenergy 5
1.4. Scopeandoutlineofthebook 6
References 8
2. Windenergyfundamentals 9
2.1. Basicsofwindpower 10
2.2. Windresourceassessment 10
2.3. Windspeeddistribution 11
2.3.1. Probabilitydensityfunctionsforwindspeed 12
2.4. Windturbinemicrositing 18
References 20
3. Paradigmsinwindforecasting 23
3.1. Introductiontotimeseries 23
3.2. Windforecasting:overview 25
3.2.1. Techniquesbasedonpredictionhorizon 26
3.2.2. Techniquesbasedonmethodsofforecasting 29
3.3. Statisticalmethods 29
3.3.1. ARMAmodel 30
3.3.2. ARIMAmodel 32
3.4. Machinelearning-basedmodels 32
3.5. Hybridwindforecastingmethods 36
References 38
4. Supervisedmachinelearningmodelsbasedonsupportvector
regression 41
4.1. Supportvectorregression 41
4.2. ε-supportvectorregression 42
4.3. Least-squaresupportvectorregression 46
4.4. Twinsupportvectorregression 50
vii
viii Contents
4.5. ε-twinsupportvectorregression 55
References 59
5. Decisiontreeensemble-basedregressionmodels 61
5.1. Randomforestregression 61
5.1.1. Monthlyrainfallprediction:acasestudy 64
5.1.2. Crudeoilpriceprediction:acasestudy 65
5.2. Gradientboostedmachines 67
5.2.1. Monthlyrainfallprediction:acasestudy 70
5.2.2. Crudeoilpriceprediction:acasestudy 71
References 72
6. Hybridmachineintelligentwindspeedforecastingmodels 75
6.1. Introduction 75
6.2. Wavelettransform 77
6.3. Frameworkofhybridforecasting 79
6.4. Resultsanddiscussion 84
6.5. Empiricalmodedecomposition-basedSVRvariantsforwindspeed
prediction 91
References 97
7. Ramppredictioninwindfarms 101
7.1. Rampeventsinscientificandengineeringactivities 101
7.1.1. Reservoirwall:acasestudyfromValsad,Gujarat 104
7.1.2. Forestriskmanagement:acasestudy 106
7.1.3. Strawberrycultivationcasestudy,Monterey,CA 107
7.1.4. Airquality:acasestudyforBogota,Colombia 109
7.2. Rampeventsinwindfarms 114
7.2.1. Rampeventerroranalysisforε-SVRandLS-SVR 115
7.2.2. RampeventerroranalysisforTSVRandε-TSVR 116
7.2.3. Casestudyforrampeventanalysis 117
7.3. Rampeventanalysisforonshoreandoffshorewindfarms 118
7.3.1. Casestudyforonshoreandoffshorewindfarms 121
7.3.2. Discussiononuncertaintiesinrampevents 131
References 137
8. Supervisedlearningforforecastinginpresenceofwindwakes 141
8.1. Introduction 141
8.2. Windwakes 144
8.2.1. Jensen’sandFrandsen’ssinglewakemodels 144
8.2.2. Proposedmodelforwindwakes 146
8.2.3. Casestudyforsingle-wakemodel 148
8.2.4. Multiple-wakemodel 150
Contents ix
8.3. Wakeeffectinwindforecasting 154
8.4. Results 156
8.4.1. Forecastingresultsforfive-turbinewindfarmlayout 157
8.4.2. Forecastingresultsfor15-turbinewindfarm 162
References 167
Epilogue 171
References 172
A. IntroductiontoRformachinelearningregression 173
A.1. DatahandlinginR 173
A.1.1. Datahandlingviavectors,lists,anddataframes 173
A.1.2. Importingdatathrough.xlsxand.csvfiles 173
A.1.3. Lineplots,histograms,andautocorrelationfunctionforwind
speedinR 174
A.1.4. Parameterestimationforwindspeedprobabilitydensity
functionsinR 176
A.2. LinearregressionanalysisinR 177
A.3. SupportvectorregressioninR 179
A.4. RandomforestregressioninR 180
A.5. GradientboostedmachinesinR 181
Index 185
List of figures
Figure1.1 Topdevelopmentsinrenewableenergysectoroverpast
decade. 2
Figure1.2 Globalnewlyaddedwindpowerinstallations. 3
Figure1.3 Coreresearchareasinwindenergy. 4
Figure1.4 Commonapplicationsofartificialintelligence. 7
Figure2.1 Frequencydistributionofwindspeeddata,PraiaFormosa,
Brazil,2017. 12
Figure2.2 WeibullPDFforλ=1anddifferentvaluesofshapeparameter. 14
Figure2.3 Weibulldistributionfitforreal-timewindspeeddata. 15
Figure2.4 GeneralizedLindleyPDFforβ=1anddifferentvaluesofα. 16
Figure2.5 GeneralizedLindleydistributionfitforreal-timewindspeed
data. 18
Figure2.6 Stacy–Mihramdistributionfitforreal-timewindspeeddata. 19
Figure2.7 Windfarmmicrositing.(A)Randomlayout.(B)Optimized
layout. 20
Figure3.1 Time-seriesplotforopeningstockpriceofFacebook,Inc. 24
Figure3.2 ACFplotforopeningstockpricesofFacebook,Inc. 24
Figure3.3 Categorizationofforecastingmethodsbasedonprediction
horizon. 26
Figure3.4 Categorizationofwindforecastingtechniquesbasedon
methods. 30
Figure3.5 Autocorrelationofwindspeedandforecastingusing
ARMA(1,1)model. 33
Figure3.6 WindspeedforecastingusingARIMA(2,1,1)model. 33
Figure3.7 Everydayexamplesofmachinelearning. 35
Figure3.8 Categorizationofmachinelearningalgorithms. 35
Figure3.9 CategorizationofHybridwindforecastingmethods. 37
Figure4.1 Diagrammaticexplanationofε-SVR. 43
Figure4.2 Kernelmappingintohigher-dimensionalspacefornonlinear
datasets. 44
Figure4.3 Schematicflowchartforε-SVRalgorithm. 45
Figure4.4 Applicationsofε-SVRprediction. 45
Figure4.5 StockpricepredictionforLarsen&ToubroInfotech. 47
Figure4.6 SchematicflowchartforLSSVRalgorithm. 47
Figure4.7 ApplicationsofLSSVRprediction. 48
Figure4.8 PowerpredictionbasedonLSSVRandpersistencealgorithm. 50
Figure4.9 SchematicflowchartforTSVRalgorithm. 50
xi