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.
Le informazioni nella sezione "Riassunto" possono far riferimento a edizioni diverse di questo titolo.
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.
Le informazioni nella sezione "Su questo libro" possono far riferimento a edizioni diverse di questo titolo.
Da: GreatBookPrices, Columbia, MD, U.S.A.
Condizione: As New. Unread book in perfect condition. Codice articolo 38704455
Quantità: 1 disponibili
Da: BuchWeltWeit Ludwig Meier e.K., Bergisch Gladbach, Germania
Taschenbuch. Condizione: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -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. Englisch. Codice articolo 9780128213537
Quantità: 2 disponibili
Da: GreatBookPrices, Columbia, MD, U.S.A.
Condizione: New. Codice articolo 38704455-n
Quantità: 1 disponibili
Da: AHA-BUCH GmbH, Einbeck, Germania
Taschenbuch. Condizione: Neu. nach der Bestellung gedruckt Neuware - Printed after ordering - 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. Codice articolo 9780128213537
Quantità: 2 disponibili
Da: Ria Christie Collections, Uxbridge, Regno Unito
Condizione: New. In English. Codice articolo ria9780128213537_new
Quantità: Più di 20 disponibili
Da: GreatBookPricesUK, Woodford Green, Regno Unito
Condizione: New. Codice articolo 38704455-n
Quantità: Più di 20 disponibili
Da: preigu, Osnabrück, Germania
Taschenbuch. Condizione: Neu. Supervised Machine Learning in Wind Forecasting and Ramp Event Prediction | Harsh S Dhiman | Taschenbuch | Einband - flex.(Paperback) | Englisch | 2020 | Elsevier Inc | EAN 9780128213537 | Verantwortliche Person für die EU: Kolibri 360 GmbH, Ettore-Bugatti-Str. 6-14, 51149 Köln, produktsicherheit[at]kolibri360[dot]de | Anbieter: preigu. Codice articolo 126701123
Quantità: 5 disponibili
Da: GreatBookPricesUK, Woodford Green, Regno Unito
Condizione: As New. Unread book in perfect condition. Codice articolo 38704455
Quantità: Più di 20 disponibili
Da: THE SAINT BOOKSTORE, Southport, Regno Unito
Paperback / softback. Condizione: New. This item is printed on demand. New copy - Usually dispatched within 5-9 working days. Codice articolo C9780128213537
Quantità: Più di 20 disponibili
Da: moluna, Greven, Germania
Condizione: New. 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 forecastingAutor/Autorin: H. Codice articolo 320002115
Quantità: Più di 20 disponibili