Estimation of Uncertainty of Wind Energy Predictions

Table Of Contents

• Cover
• Title
• Copyright
• About the author(s)/editor(s)
• About the book
• This eBook can be cited
• Contents
• Glossary
• Acronyms
• Acknowledgements
• 1. Introduction
• 1.1. Uncertainty in wind energy predictions
• 1.2. Approaches from the literature
• 2. Uncertainty in Wind Power Generation and Weather Routing
• 2.1. Weather prediction uncertainty in wind power generation (WPG)
• 2.1.1. Offshore wind power logistics
• 2.2. Prediction uncertainty in weather routing
• 2.2.1. Wind propulsion systems (WPS)
• 2.2.2. Speed power curve
• 2.2.3. Wind resistance
• 2.2.4. Wave resistance
• 2.2.5. Ship propulsion energy
• 2.2.6. Prediction uncertainty with WPS
• 2.3. Wind power generation versus weather routing
• 2.4. Weather forecasting
• 2.4.1. Numerical weather analyses and predictions
• 2.4.2. Limitations and trends in weather forecasting
• 3. Statistical Patterns of Uncertainty in Weather Predictions
• 3.1. Prediction error metrics
• 3.2. Predictions for the North Atlantic Ocean
• 3.2.1. DWD wind and wave predictions
• 3.2.2. Regional and seasonal prediction uncertainty
• 3.3. Predictions for the North and Baltic Seas
• 3.3.1. FINO measurements
• 3.3.2. Prediction uncertainty
• 3.3.3. Wave prediction uncertainty
• 3.3.4. Wind prediction uncertainty
• 3.3.5. Conclusions
• 4. Estimation of Prediction Uncertainty
• 4.1. Theoretical and empirical models
• 4.2. Ensemble prediction systems
• 4.2.1. Multi-model and multi-analyses ensembles
• 4.2.2. Super ensembles
• 4.3. Statistical methods
• 4.3.1. Probability density estimation methods
• 4.3.2. Clustering
• 4.3.3. Prediction intervals
• 4.3.4. Quantile regression
• 5. The Quantile Regression Model
• 5.1. Linear quantile regression (QR)
• 5.2. Regressors for the QR model
• 5.2.1. Principal component analysis
• 5.2.2. North Atlantic Oscillation Index (NAOI)
• 5.2.3. Other climatological indices
• 5.2.4. Statistical moments
• 5.2.5. Local Energy Distribution Moments (LEDM)
• 5.2.6. Relation between LEDM and NAOI
• 6. Implementation
• 6.1. Database with historical weather predictions
• 6.2. A* route optimization
• 6.3. Route optimization with uncertainty
• 6.4. Quantile regression
• 7. Evaluation and Results
• 7.1. Evaluation of prediction intervals
• 7.2. Application to wind-assisted sailing propulsion
• 7.3. Prediction intervals for ship propulsion energy
• 7.4. Prediction intervals for travel time
• 7.5. Uncertainty in weather routing with WaSP
• 7.6. Prediction interval benchmarks
• 7.7. Runtime discussion
• 7.8. Annual cost savings for a multi-purpose carrier
• 8. Conclusions and Outlook
• 8.1. New approach and scientific contribution
• 8.2. Outlook
• 8.2.1. Prediction uncertainty of wind turbine power output
• 8.2.2. Uncertainty in weather routing
• 8.2.3. Further applications
• Bibliography
• List of Figures
• List of Tables
• A. Parameters of Wind Propulsion Systems
• B. Parameters of the wind and wave resistance models
• C. UML diagrams of the Java implementation
• D. The Global Sea Model (GSM)