Site Adaptation of the Reanalysis Data ERA5 on the Power Prediction of Wind Farms

Abstract

Owing to the continuously improving spatial resolution and accuracy of the reanalysis data, a site adaptation case study was performed for the prediction of wind farm power output using ERA5, the 5th generation reanalysis data. The wind speed of the reanalysis data was substituted into the performance curve of the wind turbine by altitude and topographical speed up/down correction using the power law for maximizing the correlation between the predicted and actual power record. Cluster analysis was conducted to classify the wind farms into five groups, and representative onshore, inland, mountain wind farms were selected for case analysis from each cluster. Via the site adaptation of 41 wind farms in South Korea, the hourly, daily cumulative, and monthly cumulative correlation coefficients of the power output were calculated, which were 0.68, 0.79, and 0.85, respectively. In future, machine learning will be introduced for site adaptation in conjunction with the downscaling of wind resource maps by numerical weather prediction or computational fluid dynamics.

Assessing the Reliability and Optimizing Input Parameters of the NWP-CFD Downscaling Method for Generating Onshore Wind Energy Resource Maps of South Korea

“…The MCP method is utilized to adjust short-term meteorological data to long-term meteorological data [19,20], and a detailed explanation of this method can be found in Section 4.3. The long-term meteorological data used in the MCP method are ERA5 produced by the European Center for Medium-Range Weather Forecasts (ECMWF), which have a higher resolution compared to the existing reanalysis data [21][22][23]. Figure 16 visualizes the wind speed at an altitude of 100 m of the ERA5 data of the location (black dot located at the center of Figure 17, latitude-37.75, longitude-128.75) closest to the measurement location (red dot in Figure 16), and its temporal resolution is 1 h.…”