A Novel Bayesian Deep Learning Approach to the Downscaling of Wind Speed with Uncertainty Quantification

Keyphrases

• Adverse Events 33%
• Attention-based 66%
• Average Wind Speed 33%
• Bayesian Deep Learning 100%
• Climate Change Impacts 33%
• Climate Change Mitigation 33%
• Daily Averages 33%
• Deep Learning Methods 100%
• Downscaling 100%
• Effective Monitoring 33%
• Forecasting Problem 33%
• General Circulation Models 100%
• Impact Study 33%
• Local Wind Speed 33%
• Long-term Simulation 33%
• Machine Learning Techniques 33%
• Model Simulation 33%
• Modeling Data 33%
• Multivariate Time Series Forecasting 33%
• NCEP Reanalysis 33%
• New Jersey 33%
• Pennsylvania 33%
• Related Machines 33%
• Renewable Energy 33%
• Spatial Downscaling 66%
• Spatial Resolution 33%
• Suitable Location 33%
• Transformer 66%
• Uncertainty Quantification 100%
• Wildfire 33%
• Wind Dynamics 33%
• Wind Farm 33%
• Wind Speed 100%

Earth and Planetary Sciences

• Climate Change 25%
• Climate Effect 25%
• Downscaling 100%
• General Circulation Model 75%
• Machine Learning 25%
• New Jersey 25%
• Pennsylvania 25%
• Renewable Energy 25%
• Spatial Resolution 25%
• Time Series 25%
• Uncertainty Modeling 100%
• Wind Turbine 25%
• Wind Velocity 100%