Supervised Machine Learning in Wind Forecasting and Ramp Event Prediction
Published on 21. January 2020
216 pages
978-0-12-821367-4 (ISBN)
Description
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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Harsh S. Dhiman | Dipankar Deb | Valentina Emilia Balas
Supervised Machine Learning in Wind Forecasting and Ramp Event Prediction
Book
01/2020
Academic Press
€114.50
Shipment within 15-20 days
Persons
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.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.
Content
1. Introduction 2. Wind Energy Fundamentals 3. Paradigms in Wind Forecasting4. Supervised Machine Learning Models based on Support Vector Regression5. Decision tree ensemble-based Regression Models6. Hybrid Machine Intelligent Wind Speed Forecasting Models7. Ramp Prediction in Wind Farms8. Supervised Learning for Forecasting in presence of Wind WakesA. Introduction to R for Machine Learning RegressionA.1 Data handling in RA.2 Linear Regression Analysis in RA.3 Support vector regression in R A.4 Random Forest Regression in R A.5 Gradient boosted machines in R
