Supervised Machine Learning in Wind Forecasting and Ramp Event Prediction

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Supervised Machine Learning in Wind Forecasting and Ramp Event Prediction - Harsh S. Dhiman, Dipankar Deb 2020 PDF Academic Press BOOKS PROGRAMMING

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Supervised Machine Learning in Wind Forecasting and Ramp Event Prediction

Author: Harsh S. Dhiman, Dipankar Deb
Year: 2020
Format: PDF
File size: 10.1 MB
Language: ENG

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Supervised Machine Learning in Wind Forecasting and Ramp Event Prediction Authors: [List the names of the authors] 2020 206 Publisher: [Name of publisher] Summary: This book provides an up-to-date overview of the broad area of wind generation and forecasting, focusing on the role and need of machine learning (ML) in this emerging field of knowledge. It covers various regression models and signal decomposition techniques, including least squares twin support and random forest regression, all with supervised ML. The specific topics of ramp event prediction and wake interactions are addressed, along with forecasted performance measures and validation results. Long Description: The development of modern technology has led to a significant increase in the demand for renewable energy sources, such as wind power. However, wind power generation is affected by various factors, such as weather conditions, turbulence, and wake interactions, which can cause fluctuations in power output and affect the stability of the power grid. To address these challenges, researchers have been exploring the use of machine learning (ML) techniques to improve wind forecasting and ramp event prediction.

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