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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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At least 307 records · Page 17
All sourcesNASA NTRSDOE OSTI
DOE OSTI2014

Validation of Power Output for the WIND Toolkit

Renewable energy integration studies require wind data sets of high quality with realistic representations of the variability, ramping characteristics, and forecast performance for current wind power plants. The Wind Integration National Data Set (WIND) Toolkit is meant to be an update for and expansion of the original data sets created for the weather years from 2004 through 2006 during the Western Wind and Solar Integration Study and the Eastern Wind Integration Study. The WIND Toolkit expands these data sets to include the entire continental United States, increasing the total number of sites represented, and it includes the weather years from 2007 through 2012. In addition, the WIND Toolkit has a finer resolution for both the temporal and geographic dimensions. Three separate data sets will be created: a meteorological data set, a wind power data set, and a forecast data set. This report describes the validation of the wind power data set.

17 WIND ENERGY↗
DOE OSTI2022

Evaluating wind speed and power forecasts for wind energy applications using an open-source and systematic validation framework

Building on the verification and validation work developed under the Second Wind Forecast Improvement Project, this work exhibits the value of a consistent procedure to evaluate wind power forecasts. We established an open-source Python code base tailored for wind speed and wind power forecast validation, WE-Validate. The code base can evaluate model forecasts with observations in a coherent manner. To demonstrate the systematic validation framework of WE-Validate, we designed and hosted a forecast evaluation benchmark exercise. We invited forecast providers in industry and academia to participate and submit forecasts for two case studies. We then evaluated the submissions with WE-Validate. Our findings suggest that ensemble means have reasonable skills in time series forecasting, whereas they are often inferior to single ensemble members in wind ramp forecasting. Adopting a voting scheme in ramp forecasting that allows ensemble members to detect ramps independently leads to satisfactory skill scores. Throughout this document, we also emphasize the importance of using statistically robust and resistant metrics as well as equitable skill scores in forecast evaluation.

17 WIND ENERGY↗