Supervised Machine Learning in Wind Forecasting and Ramp Event Prediction
Jan 2020 · Academic Press
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216
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About this ebook
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
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 decompos...
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About the author
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 Full Professor in the Department of Automatics and Applied Software, Faculty of Engineering, at Aurel Vlaicu University of Arad, Romania. She holds a PhD in Applied Electronics and Telecommunications from the Polytechnic University of Timisoara. Her research spans intelligent systems, computational intelligence, applied informatics, and engineering applications. Dr. Balas serves as Editor-in-Chief of International Journal of Advanced Intelligence Paradigms and International Journal of Computational Systems Engineering, and is a member of the editorial boards of several international journals. She is actively involved in the evaluation of national and international research projects and doctoral theses, and contributes to scholarly initiatives that support the advancement of intelligent and computational systems research.
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 Saya...
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