Supervised Machine Learning in Wind Forecasting and Ramp Event Prediction

This book PDF is perfect for those who love Machine learning genre, written by Harsh S. Dhiman and published by Academic Press which was released on 20 January 2020 with total hardcover pages 216. You could read this book directly on your devices with pdf, epub and kindle format, check detail and related Supervised Machine Learning in Wind Forecasting and Ramp Event Prediction books below.

Supervised Machine Learning in Wind Forecasting and Ramp Event Prediction
Author : Harsh S. Dhiman
File Size : 48,9 Mb
Publisher : Academic Press
Language : English
Release Date : 20 January 2020
ISBN : 9780128213537
Pages : 216 pages
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Supervised Machine Learning in Wind Forecasting and Ramp Event Prediction by Harsh S. Dhiman Book PDF Summary

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

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

Get Book
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