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. Chapter 1: Introduction - p. 1 2. Chapter 2: Wind energy fundamentals - p. 9 3. Chapter 3: Paradigms in wind forecasting - p. 23 4. Chapter 4: Supervised machine learning models based on support vector regression - p. 41 5. Chapter 5: Decision tree ensemble-based regression models - p. 61 6. Chapter 6: Hybrid machine intelligent wind speed forecasting models - p. 75 7. Chapter 7: Ramp prediction in wind farms - p. 101 8. Chapter 8: Supervised learning for forecasting in presence of wind wakes - p. 141 9. Epilogue - p. 171 10. Appendix A: Introduction to R for machine learning regression - p. 173 11. Index - p. 185
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. Chapter 1: Introduction - p. 1 2. Chapter 2: Wind energy fundamentals - p. 9 3. Chapter 3: Paradigms in wind forecasting - p. 23 4. Chapter 4: Supervised machine learning models based on support vector regression - p. 41 5. Chapter 5: Decision tree ensemble-based regression models - p. 61 6. Chapter 6: Hybrid machine intelligent wind speed forecasting models - p. 75 7. Chapter 7: Ramp prediction in wind farms - p. 101 8. Chapter 8: Supervised learning for forecasting in presence of wind wakes - p. 141 9. Epilogue - p. 171 10. Appendix A: Introduction to R for machine learning regression - p. 173 11. Index - p. 185
