An integrated wind-forecast system based on the weather research and forecasting model, Kalman filter, and data assimilation with nacelle-wind observation.

To forecast wind speed at hub-height is a challenging task for wind energy application, especially in Japan where the terrain feature is very complex and large fluctuations are observed in surface wind field. In this study, an integrated system to predict the hub-height wind speed has been developed by combining data assimilation and Kalman filter with the high resolution Weather Research and Forecasting (WRF) model. Assimilating the nacelle wind data (quality-controlled) and the Kalman filter algorithm effectively improves accuracy of the WRF model forecast by optimizing initial condition and post-processing the model output, respectively. It is found that the WRF model forecasts can be markedly improved after assimilating the nacelle wind data through the Gridpoint Statistical Interpolation analysis system, with the relative improvements of 34.3%, 23.9%, and 8.8% in ME (mean error), RMSE (root mean square error), and IA (index of agreement), respectively. The implementation of the Kalman filter can significantly reduce ME and RMSE while increases the value of IA as well. Further improvement can be achieved if the Kalman filter and nacelle wind data assimilation are implemented simultaneously. It is observed that the role of the Kalman filter is more dominant for the wind band of rated out speeds, while data assimilation is effective in reducing the random errors and becomes more important in rare or extreme weather conditions. Both data assimilation and Kalman filter modules apply the nacelle wind data which is routinely available, so the system can be easily adopted in different wind farm sites for operational use. [ABSTRACT FROM AUTHOR]