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Supervised machine learning in wind forecasting and ramp event prediction /
"[A]n 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 suppo...
| Clasificación: | Libro Electrónico |
|---|---|
| Autores principales: | , , |
| Formato: | Electrónico eBook |
| Idioma: | Inglés |
| Publicado: |
London, United Kingdom :
Academic Press,
2020.
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| Colección: | Wind energy engineering series.
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| Temas: | |
| Acceso en línea: | Texto completo |
Tabla de Contenidos:
- Introduction: Renewable energy focus
- Wind energy : issues and challenges
- Machine learning in allied areas of wind energy
- Scope and outline of the book
- Wind energy fundamentals: Basics of wind power
- Wind resource assessment
- Wind turbine micrositing
- Paradigms in wind forecasting: Introduction to time series
- Wind forecasting overview
- Statistical methods
- Machine learning-based models
- Hybrid wind forecasting methods
- Supervised machine learning models based on support vector regression: Support vector regression
- ₀-support vector regression
- Least-square support vector regression
- Twin support vector regression
- ₀-twin support vector regression
- Decision tree ensemble-based regression models: Random forest regression
- Gradient boosted machines
- Hybrid machine intelligent wind speed forecasting models: Introduction
- Wavelet transform
- Framework of hybrid forecasting
- Results and discussion
- Empirical mode of decomposition-based SVR variants for wind speed prediction
- Ramp prediction in wind farms: Ramp events in scientific and engineering activities
- Ramp events in wind farms
- Ramp event analysis for onshore and offshore wind farms
- Supervised learning for forecasting in presence of wind wakes: Introduction
- Wind wakes
- Wake effect in wind forecasting
- Results
- Epilogue
- Introduction to R for machine learning regression: Data handling in R
- Linear regression analysis in R
- Support vector regression in R
- Random forest regression in R
- Gradient boosted machines in R.