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Forecasting Wind Power Quantiles Using Conditional Kernel Estimation
James W. Taylor, Jooyoung Jeon
Management
University of Oxford
Research output
:
Contribution to journal
›
Article
›
peer-review
19
Citations (SciVal)
266
Downloads (Pure)
Overview
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Dive into the research topics of 'Forecasting Wind Power Quantiles Using Conditional Kernel Estimation'. Together they form a unique fingerprint.
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Mathematics
Conditionals
100%
Quantile
100%
Quantile Regression
33%
Wind Speed
33%
Wind Velocity
33%
Distributional Assumption
16%
Objective Function
16%
Kernel Density
16%
Kernel Density Estimation
16%
Density Based Approaches
16%
Earth and Planetary Sciences
Quantile
100%
Wind Power Forecasting
100%
Wind Power
100%
Wind Velocity
25%
Compressed Air Motors
25%
Engineering
Wind Power
100%
Quantile
100%
Power Density
33%
Compressed Air Motors
22%
Objective Function
11%
Electricity System
11%
Efficient Management
11%
Good Result
11%
Forecast Error
11%
Economics, Econometrics and Finance
Density Based Approaches
100%
Kernel Estimation
100%
Agricultural and Biological Sciences
Wind Power
100%
Wind Speed
22%
Compressed Air Motors
22%
Europeans
11%