Supervised Machine Learning in Wind Forecasting and Ramp Event Prediction

By: Dhiman, Deb, Balas

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Paperback | 31 January 2020 | Edition Number 1

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Paperback
216 Pages

Dimensions(cm)
22.86 x 15.24 x 1.17

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Supervised Machine Learning in Wind Forecasting and Ramp Event Prediction provides an up to date overview of 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, with the authors providing a variety of wind farm datasets and conducted statistical tests to ascertain the robustness of the presented prediction models.

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 in renewable energy and 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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