Generating wind power scenarios for probabilistic ramp event prediction using multivariate statistical post-processing

Worsnop, Rochelle P.; Scheuerer, Michael; Hamill, Thomas M.; Lundquist, Julie K.

doi:https://doi.org/10.5194/wes-3-371-2018

Articles | Volume 3, issue 1

https://doi.org/10.5194/wes-3-371-2018

© Author(s) 2018. This work is distributed under
the Creative Commons Attribution 4.0 License.

https://doi.org/10.5194/wes-3-371-2018

© Author(s) 2018. This work is distributed under
the Creative Commons Attribution 4.0 License.

Articles | Volume 3, issue 1

Research article

|

14 Jun 2018

Research article |

| 14 Jun 2018

Generating wind power scenarios for probabilistic ramp event prediction using multivariate statistical post-processing

Rochelle P. Worsnop, Michael Scheuerer, Thomas M. Hamill, and Julie K. Lundquist

Data sets

135-m Tower Data NWTC https://wind.nrel.gov/MetData/135mData/M5Twr/

Short summary

This paper uses four statistical methods to generate probabilistic wind speed and power ramp forecasts from the High Resolution Rapid Refresh model. The results show that these methods can provide necessary uncertainty information of power ramp forecasts. These probabilistic forecasts can aid in decisions regarding power production and grid integration of wind power.