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Supervised Machine Learning in Wind Forecasting and Ramp Event Prediction. Wind Energy Engineering

  • ID: 4844381
  • Book
  • January 2020
  • 216 Pages
  • Elsevier Science and Technology
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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

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1. Introduction 2. Wind Energy Fundamentals 3. Paradigms in Wind Forecasting 4. Supervised Machine Learning Models based on Support Vector Regression 5. Decision tree ensemble-based Regression Models 6. Hybrid Machine Intelligent Wind Speed Forecasting Models 7. Ramp Prediction in Wind Farms 8. Supervised Learning for Forecasting in presence of Wind Wakes A. Introduction to R for Machine Learning Regression A.1 Data handling in R A.2 Linear Regression Analysis in R A.3 Support vector regression in R A.4 Random Forest Regression in R A.5 Gradient boosted machines in R

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Dhiman, Harsh S.
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 Master's 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.
Deb, Dipankar
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.
Balas, Valentina Emilia
Valentina E. Balas is currently Full Professor in the Department of Automatics and Applied Software at the Faculty of Engineering, "Aurel Vlaicu” University of Arad, Romania. She holds a Ph.D. in Applied Electronics and Telecommunications from Polytechnic University of Timisoara. Dr. Balas is author of more than 300 research papers in refereed journals and International Conferences. Her research interests are in Intelligent Systems, Fuzzy Control, Soft Computing, Smart Sensors, Information Fusion, Modeling and Simulation.She is the Editor-in Chief to International Journal of Advanced Intelligence Paradigms (IJAIP) and to International Journal of Computational Systems Engineering (IJCSysE), member in Editorial Board member of several national and international journals and is evaluator expert for national, international projects and PhD Thesis
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