Wind Forecasting in Railway Engineering presents core and leading-edge technologies in wind forecasting for railway engineering. The title brings together wind speed forecasting and railway wind engineering, offering solutions from both fields. Key technologies are presented, along with theories, modeling steps and comparative analyses of forecasting technologies. Each chapter presents case studies and applications, including typical applications and key issues, analysis of wind field characteristics, optimization methods for the placement of a wind anemometer, single-point time series along railways, deep learning algorithms on single-point wind forecasting, reinforcement learning algorithms, ensemble single-point wind forecasting methods, spatial wind, and data-driven spatial-temporal wind forecasting algorithms.
This important book offers practical solutions for railway safety, by bringing together the latest technologies in wind speed forecasting and railway wind engineering into a single volume.
1. Chapter 1 Introduction - p. 1 2. Chapter 2 Analysis of flow field characteristics along railways - p. 45 3. Chapter 3 Description of single-point wind time series along railways - p. 69 4. Chapter 4 Single-point wind forecasting methods based on deep learning - p. 137 5. Chapter 5 Single-point wind forecasting methods based on reinforcement learning - p. 177 6. Chapter 6 Single-point wind forecasting methods based on ensemble modeling - p. 215 7. Chapter 7 Description methods of spatial wind along railways - p. 251 8. Chapter 8 Data-driven spatial wind forecasting methods along railways - p. 283 9. 8.1: Introduction - p. 284 10. 8.2: Wind data description - p. 285 11. 8.3: Spatial wind forecasting algorithm based on statistical model - p. 286 12. 8.4: Spatial wind forecasting algorithm based on intelligent model - p. 295 13. 8.5: Spatial wind forecasting algorithm based on deep learning model - p. 307 14. 8.6: Summary and outlook - p. 317 15. References - p. 318 16. Index - p. 321
Wind Forecasting in Railway Engineering presents core and leading-edge technologies in wind forecasting for railway engineering. The title brings together wind speed forecasting and railway wind engineering, offering solutions from both fields. Key technologies are presented, along with theories, modeling steps and comparative analyses of forecasting technologies. Each chapter presents case studies and applications, including typical applications and key issues, analysis of wind field characteristics, optimization methods for the placement of a wind anemometer, single-point time series along railways, deep learning algorithms on single-point wind forecasting, reinforcement learning algorithms, ensemble single-point wind forecasting methods, spatial wind, and data-driven spatial-temporal wind forecasting algorithms.
This important book offers practical solutions for railway safety, by bringing together the latest technologies in wind speed forecasting and railway wind engineering into a single volume.
1. Chapter 1 Introduction - p. 1 2. Chapter 2 Analysis of flow field characteristics along railways - p. 45 3. Chapter 3 Description of single-point wind time series along railways - p. 69 4. Chapter 4 Single-point wind forecasting methods based on deep learning - p. 137 5. Chapter 5 Single-point wind forecasting methods based on reinforcement learning - p. 177 6. Chapter 6 Single-point wind forecasting methods based on ensemble modeling - p. 215 7. Chapter 7 Description methods of spatial wind along railways - p. 251 8. Chapter 8 Data-driven spatial wind forecasting methods along railways - p. 283 9. 8.1: Introduction - p. 284 10. 8.2: Wind data description - p. 285 11. 8.3: Spatial wind forecasting algorithm based on statistical model - p. 286 12. 8.4: Spatial wind forecasting algorithm based on intelligent model - p. 295 13. 8.5: Spatial wind forecasting algorithm based on deep learning model - p. 307 14. 8.6: Summary and outlook - p. 317 15. References - p. 318 16. Index - p. 321
