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E-raamat: Supervised Machine Learning in Wind Forecasting and Ramp Event Prediction

(Professor in Electrical Engineering, Institute of Infrastructure Technology Research and Management (IITRAM), A), , (Department of Electrical Engineering, Institute of Infrastructure Technology Research and Management (IITRAM), Ahmedabad)
  • Formaat: PDF+DRM
  • Sari: Wind Energy Engineering
  • Ilmumisaeg: 21-Jan-2020
  • Kirjastus: Academic Press Inc
  • Keel: eng
  • ISBN-13: 9780128213674
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Supervised Machine Learning in Wind Forecasting and Ramp Event PredictionSuurem pilt
  • Formaat: PDF+DRM
  • Sari: Wind Energy Engineering
  • Ilmumisaeg: 21-Jan-2020
  • Kirjastus: Academic Press Inc
  • Keel: eng
  • ISBN-13: 9780128213674
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Supervised Machine Learning in Wind Forecasting and Ramp Event Prediction provides an up-to- date overview on the broad area of wind generation and forecasting, with a focus on the role and need of Machine Learning in this emerging field of knowledge. Various regression models and signal decomposition techniques are presented and analyzed, including least-square, twin support and random forest regression, all with supervised Machine Learning. The specific topics of ramp event prediction and wake interactions are addressed in this book, along with forecasted performance.

Wind speed forecasting has become an essential component to ensure power system security, reliability and safe operation, making this reference useful for all researchers and professionals researching renewable energy, wind energy forecasting and generation.

  • Features various supervised machine learning based regression models
  • Offers global case studies for turbine wind farm layouts
  • Includes state-of-the-art models and methodologies in wind forecasting
List of figures
xi
List of tables
xv
Biography xvii
Preface xix
Acknowledgments xxi
Acronyms xxiii
1 Introduction
1(8)
1.1 Renewable energy focus
1(2)
1.2 Wind energy: issues and challenges
3(2)
1.3 Machine learning in allied areas of wind energy
5(3)
1.4 Scope and outline of the book 6 References
8(1)
2 Wind energy fundamentals
9(14)
2.1 Basics of wind power
10(1)
2.2 Wind resource assessment
10(1)
2.3 Wind speed distribution
11(7)
2.3.1 Probability density functions for wind speed
12(6)
2.4 Wind turbine micrositing
18(2)
References
20(3)
3 Paradigms in wind forecasting
23(18)
3.1 Introduction to time series
23(2)
3.2 Wind forecasting: overview
25(4)
3.2.1 Techniques based on prediction horizon
26(3)
3.2.2 Techniques based on methods of forecasting
29(1)
3.3 Statistical methods
29(3)
3.3.1 ARMA model
30(2)
3.3.2 ARIMA model
32(1)
3.4 Machine learning-based models
32(4)
3.5 Hybrid wind forecasting methods
36(2)
References
38(3)
4 Supervised machine learning models based on support vector regression
41(20)
4.1 Support vector regression
41(1)
4.2 e-support vector regression
42(4)
4.3 Least-square support vector regression
46(4)
4.4 Twin support vector regression
50(5)
4.5 e-twin support vector regression
55(4)
References
59(2)
5 Decision tree ensemble-based regression models
61(14)
5.1 Random forest regression
61(6)
5.1.1 Monthly rainfall prediction: a case study
64(1)
5.1.2 Crude oil price prediction: a case study
65(2)
5.2 Gradient boosted machines
67(8)
5.2.1 Monthly rainfall prediction: a case study
70(2)
5.2.2 Crude oil price prediction: a case study 71 References
72(3)
6 Hybrid machine intelligent wind speed forecasting models
75(26)
6.1 Introduction
75(2)
6.2 Wavelet transform
77(2)
6.3 Framework of hybrid forecasting
79(5)
6.4 Results and discussion
84(7)
6.5 Empirical mode decomposition-based SVR variants for wind speed prediction
91(6)
References
97(4)
7 Ramp prediction in wind farms
101(40)
7.1 Ramp events in scientific and engineering activities
101(13)
7.1.1 Reservoir wall: a case study from Valsad, Gujarat
104(2)
7.1.2 Forest risk management: a case study
106(1)
7.1.3 Strawberry cultivation case study, Monterey, CA
107(2)
7.1.4 Air quality: a case study for Bogota, Colombia
109(5)
7.2 Ramp events in wind farms
114(4)
7.2.1 Ramp event error analysis for fi-SVR and LS-SVR
115(1)
7.2.2 Ramp event error analysis for TSVR and e-TSVR
116(1)
7.2.3 Case study for ramp event analysis
117(1)
7.3 Ramp event analysis for onshore and offshore wind farms
118(19)
7.3.1 Case study for onshore and offshore wind farms
121(10)
7.3.2 Discussion on uncertainties in ramp events
131(6)
References
137(4)
8 Supervised learning for forecasting in presence of wind wakes
141(30)
8.1 Introduction
141(3)
8.2 Wind wakes
144(10)
8.2.1 Jensen's and Frandsen's single wake models
144(2)
8.2.2 Proposed model for wind wakes
146(2)
8.2.3 Case study for single-wake model
148(2)
8.2.4 Multiple-wake model
150(4)
8.3 Wake effect in wind forecasting
154(2)
8.4 Results
156(11)
8.4.1 Forecasting results for five-turbine wind farm layout
157(5)
8.4.2 Forecasting results for 15-turbine wind farm
162(5)
References
167(4)
Epilogue
171(2)
References
172(1)
A Introduction to R for machine learning regression
173(12)
A.1 Data handling in R
173(4)
A.1.1 Data handling via vectors, lists, and data frames
173(1)
A.1.2 Importing data through xlsx and csvfiles 173 A.1.3. Line plots, histograms, and autocorrelation function for wind speed in R
174(2)
A.1.4 Parameter estimation for wind speed probability density functions in R
176(1)
A.2 Linear regression analysis in R
177(2)
A.3 Support vector regression in R
179(1)
A.4 Random forest regression in R
180(1)
A.5 Gradient boosted machines in R
181(4)
Index 185
Harsh S. Dhiman is a research scholar in Department of Electrical Engineering from Institute of Infrastructure Technology Research and Management (IITRAM), Ahmedabad, India. He obtained his Masters degree in Electrical Power Engineering from Faculty of Technology & Engineering, The Maharaja Sayajirao University of Baroda, Vadodara, India in 2016 and B. Tech in Electrical Engineering from Institute of Technology, Nirma University, Ahmedabad, India in 2014. His current research interests include Hybrid operation of wind farms, Hybrid wind forecasting techniques and Wake management in wind farms. Dipankar Deb completed his Ph.D. from University of Virginia, Charlottesville under the supervision of Prof.Gang Tao, IEEE Fellow and Professor in the department of ECE in 2007. In 2017, he was elected to be a IEEE Senior Member. He has served as a Lead Engineer at GE Global Research Bengaluru (2012-15) and as an Assistant Professor in EE, IIT Guwahati 2010-12. Presently, he is a Professor in Electrical Engineering at Institute of Infrastructure Technology Research and Management (IITRAM), Ahmedabad. His research interests include Control theory, Stability analysis and Renewable energy systems. Valentina Emilia Balas is currently a Full Professor in the Department of Automatics and Applied Software at the Faculty of Engineering, Aurel Vlaicu” University of Arad, Romania. She holds a PhD cum Laude in Applied Electronics and Telecommunications from the Polytechnic University of Timisoara. Dr. Balas is the author of more than 350 research papers. She is the Editor-in-Chief of the 'International Journal of Advanced Intelligence Paradigms' and the 'International Journal of Computational Systems Engineering', an editorial board member for several other national and international publications, and an expert evaluator for national and international projects and PhD theses.
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