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Supervised Machine Learning in Wind Forecasting and Ramp Event Prediction
Dynamic reference on machine learning applications of regression for forecasting short-term wind energy
Harsh S. Dhiman (Author), Dipankar Deb (Author), Valentina Emilia Balas (Author)
9780128213537, Elsevier Science
Paperback, published 31 January 2020
216 pages
22.9 x 15.1 x 1.5 cm, 0.29 kg
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.
1. Introduction 2. Wind Energy Fundamentals 3. Paradigms in Wind Forecasting4. Supervised Machine Learning Models based on Support Vector Regression5. Decision tree ensemble-based Regression Models6. Hybrid Machine Intelligent Wind Speed Forecasting Models7. Ramp Prediction in Wind Farms8. Supervised Learning for Forecasting in presence of Wind WakesA. Introduction to R for Machine Learning RegressionA.1 Data handling in RA.2 Linear Regression Analysis in RA.3 Support vector regression in R A.4 Random Forest Regression in R A.5 Gradient boosted machines in R
Subject Areas: Alternative & renewable energy sources & technology [THX]
