A consensus forecasting approach for improved turbine hub height wind speed predictions
Myers, W., Wiener, G., Linden, S., & Haupt, S. E. (2011). A consensus forecasting approach for improved turbine hub height wind speed predictions. In WINDPOWER 2011 Conference & Exhibition. American Wind Energy Association (AWEA): Anaheim, CA, US.
The National Center for Atmospheric Research (NCAR) has developed a wind prediction system for Xcel Energy, the power company with the largest wind capacity in the United States (Johnson et al. 2011). The wind power forecasting system includes advanced modeling capabilities, data assimilation, no... Show moreThe National Center for Atmospheric Research (NCAR) has developed a wind prediction system for Xcel Energy, the power company with the largest wind capacity in the United States (Johnson et al. 2011). The wind power forecasting system includes advanced modeling capabilities, data assimilation, nowcasting, and statistical post-processing technologies. The system ingests both publicly available and specialized model data and weather and wind farm observations. NCAR produces a deterministic mesoscale wind forecast of hub height winds on a very fine resolution grid using the Weather Research and Forecasting (WRF) model, run using the Real Time Four Dimensional Data Assimilation (RTFDDA) system (Liu et al. 2008). In addition, a 30 member ensemble system is run to both improve forecast accuracy and provide an indication of forecast uncertainty. The deterministic and ensemble model output plus data from various global and regional models are ingested by NCAR’s Dynamic Integrated Forecast System (DICast®), a machine learning system. DICast® produces forecasts of wind speed for each wind turbine. These wind forecasts are then fed into a power conversion algorithm that has been empirically derived for each Xcel power connection node. This basic system has consistently improved Xcel’s ability to optimize the economics of incorporating wind energy into their power system. Show less