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nonparametric forecast error density estimators
Tác giả:
Xuất bản: Washington DC Oak Ridge Tenn: United States National Nuclear Security Administration Distributed by the Office of Scientific and Technical Information US Dept of Energy, 2017
Bộ sưu tập: Metadata
ddc:  656.6
 
Simulating wind power forecast error distributions for spatially aggregated wind power plants [electronic resource]
Tác giả:
Xuất bản: Washington DC Oak Ridge Tenn: United States Dept of Energy Office of Energy Efficiency and Renewable Energy Distributed by the Office of Scientific and Technical Information US Dept of Energy, 2019
Bộ sưu tập: Metadata
ddc:  621.45
 
Utilizing physics-based input features within a machine learning model to predict wind speed forecasting error [electronic resource]
Tác giả:
Xuất bản: Richland Wash Oak Ridge Tenn: Pacific Northwest National Laboratory US Distributed by the Office of Scientific and Technical Information US Dept of Energy, 2021
Bộ sưu tập: Metadata
ddc:  333.9
 
The use of real-time off-site observations as a methodology for increasing forecast skill in prediction of large wind power ramps one or more hours ahead of their impact on a wind plant. [electronic
Tác giả:
Xuất bản: Oak Ridge Tenn: Distributed by the Office of Scientific and Technical Information US Dept of Energy, 2012
Bộ sưu tập: Metadata
ddc:  621.5
 
The use of real-time off-site observations as a methodology for increasing forecast skill in prediction of large wind power ramps one or more hours ahead of their impact on a wind plant. [electronic
Tác giả:
Xuất bản: Oak Ridge Tenn: Distributed by the Office of Scientific and Technical Information US Dept of Energy, 2012
Bộ sưu tập: Metadata
ddc:  621.531
 
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