Supervised Machine Learning in Wind Forecasting and Ramp Event Prediction
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…
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Produktdetails
Weitere Autoren: Deb, Dipankar / Balas, Valentina Emilia
- ISBN: 978-0-12-821367-4
- EAN: 9780128213674
- Produktnummer: 35965840
- Verlag: Elsevier Science & Techn.
- Sprache: Englisch
- Erscheinungsjahr: 2020
- Seitenangabe: 216 S.
- Plattform: EPUB
- Masse: 27'417 KB
Über den Autor
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.
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