Wind Forecasting in Railway Engineering
- 1st Edition - June 17, 2021
- Imprint: Elsevier
- Author: Hui Liu
- Language: English
- Paperback ISBN:9 7 8 - 0 - 1 2 - 8 2 3 7 0 6 - 9
- eBook ISBN:9 7 8 - 0 - 1 2 - 8 2 3 7 0 7 - 6
Wind Forecasting in Railway Engineering presents core and leading-edge technologies in wind forecasting for railway engineering. The title brings together wind speed forecasti… Read more
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Request a sales quoteWind 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.
- Presents the core technologies and most advanced developments in wind forecasting for railway engineering
- Gives case studies and experimental designs, demonstrating real-world applications
- Introduces cutting-edge deep learning and reinforcement learning methods
- Combines the latest thinking from wind engineering and railway engineering
- Offers a complete solution to wind forecasting in railway engineering for the safety of running trains
Researchers in wind forecasting and railway engineering, engineering technicians, railway engineers, managerial staff in railway engineering; graduate researchers working in railway engineering, rail transit engineering, data science, and forecasting
- Cover image
- Title page
- Table of Contents
- Copyright
- List of figures
- List of tables
- Preface
- Acknowledgments
- Nomenclature list
- Chapter 1. Introduction
- 1.1. Overview of wind forecasting in train wind engineering
- 1.2. Typical scenarios of railway wind engineering
- 1.3. Key technical problems in wind signal processing
- 1.4. Wind forecasting technologies in railway wind engineering
- 1.5. Scope of this book
- Chapter 2. Analysis of flow field characteristics along railways
- 2.1. Introduction
- 2.2. Analysis of spatial characteristics of railway flow field
- 2.3. Analysis of seasonal characteristics of railway flow field
- 2.4. Summary and outlook
- Chapter 3. Description of single-point wind time series along railways
- 3.1. Introduction
- 3.2. Wind anemometer layout optimization methods along railways
- 3.3. Single-point wind speed–wind direction seasonal analysis
- 3.4. Single-point wind speed–wind direction heteroscedasticity analysis
- 3.5. Various single-point wind time series description algorithms
- 3.6. Description accuracy evaluation indicators
- 3.7. Summary and outlook
- Chapter 4. Single-point wind forecasting methods based on deep learning
- 4.1. Introduction
- 4.2. Wind data description
- 4.3. Single-point wind speed forecasting algorithm based on LSTM
- 4.4. Single-point wind speed forecasting algorithm based on GRU
- 4.5. Single-point wind speed direction algorithm based on Seriesnet
- 4.6. Summary and outlook
- Chapter 5. Single-point wind forecasting methods based on reinforcement learning
- 5.1. Introduction
- 5.2. Wind data description
- 5.3. Single-point wind speed forecasting algorithm based on Q-learning
- 5.4. Single-point wind speed forecasting algorithm based on deep reinforcement learning
- 5.5. Summary and outlook
- Chapter 6. Single-point wind forecasting methods based on ensemble modeling
- 6.1. Introduction
- 6.2. Wind data description
- 6.3. Single-point wind speed forecasting algorithm based on multi-objective ensemble
- 6.4. Single-point wind speed forecasting algorithm based on stacking
- 6.5. Single-point wind direction forecasting algorithm based on boosting
- 6.6. Summary and outlook
- Chapter 7. Description methods of spatial wind along railways
- 7.1. Introduction
- 7.2. Spatial wind correlation analysis
- 7.3. Spatial wind description based on WRF
- 7.4. Description accuracy evaluation indicators
- 7.5. Summary and outlook
- Chapter 8. Data-driven spatial wind forecasting methods along railways
- 8.1. Introduction
- 8.2. Wind data description
- 8.3. Spatial wind forecasting algorithm based on statistical model
- 8.4. Spatial wind forecasting algorithm based on intelligent model
- 8.5. Spatial wind forecasting algorithm based on deep learning model
- 8.6. Summary and outlook
- Index
- Edition: 1
- Published: June 17, 2021
- No. of pages (Paperback): 362
- No. of pages (eBook): 362
- Imprint: Elsevier
- Language: English
- Paperback ISBN: 9780128237069
- eBook ISBN: 9780128237076
HL
Hui Liu
He holds joint PhD degrees from the Central South University and from Rostock University in Germany, and also obtained his habilation in Automation Engineering from the University of Rostock. He has published over 40 papers in leading journals, as well as two monographs. He holds 35 patents in China on transportation robotics and artificial intelligence, and has received numerous academic awards. He has extensive research and industry experience both in rail transit and in robotics.
Affiliations and expertise
Professor of Robotics and Artificial Intelligence, and Vice-dean, Faculty of Transportation Engineering, Central South University, Changsha, ChinaRead Wind Forecasting in Railway Engineering on ScienceDirect