Uncertainty Quantification of a Coupled Model for Wind Prediction at a Wind Farm in Japan

Reliable and accurate short-term prediction of wind speed at hub height is very important to optimize the integration of wind energy into existing electrical systems. To this end, a coupled model based on the Weather Research Forecasting (WRF) model and Open Source Field Operation and Manipulation (OpenFOAM) Computational Fluid Dynamics (CFD) model is proposed to improve the forecast of the wind fields over complex terrain regions. The proposed model has been validated with the quality-controlled observations of 15 turbine sites in a target wind farm in Japan. The numerical results show that the coupled model provides more precise forecasts compared to the WRF alone forecasts, with the overall improvements of 26%, 22% and 4% in mean error (ME), root mean square error (RMSE) and correlation coefficient (CC), respectively. As the first step to explore further improvement of the coupled system, the polynomial chaos expansion (PCE) approach is adopted to quantitatively evaluate the effects of several parameters in the coupled model. The statistics from the uncertainty quantification results show that the uncertainty in the inflow boundary conditions to the CFD model affects more dominantly the hub-height wind prediction in comparison with other parameters in the turbulence model, which suggests an effective approach to parameterize and assimilate the coupling interface of the model

Multivariable neural network to postprocess short‐term, hub‐height wind forecasts

Abstract This work introduces a novel error correction method for short‐term, hub‐height wind speed forecasting systems aimed at power output prediction. We present a multivariable neural network that is trained to reduce the error in wind speed predictions out of a numerical weather prediction (NWP) model, by exploiting hidden information in additional atmospheric variables, that is, wind direction, temperature, and pressure. The unique layout of the network was influenced by that of denoising autoencoders, and their ability to learn mapping functions. The predicted values from the NWP model, which incorporate errors due to numerical discretization, inaccuracies in initial/boundary conditions and parametrizations, complex terrain features, etc., are mapped to a more accurate prediction in which the errors have been reduced. To show the performance of the proposed model, training and validation are carried out with 4 years of forecasted and observed data for fifteen sites in a wind farm in Awaji Island, Japan, in a challenging zone with complex topography and therefore complicated, highly fluctuating wind patterns. Moreover, a single variable (i.e., wind speed) network is also implemented in order to expose the contribution and usefulness of including additional atmospheric variables. The results show a considerable reduction in the root mean square error as well as an increase in the correlation coefficient. As expected, it is found that multiple meteorological variables as inputs offer a huge advantage when compared with the equivalent single‐variable correction method

A Novel Hybrid Model of WRF and Clearness Index-Based Kalman Filter for Day-Ahead Solar Radiation Forecasting

Day-ahead forecasting of solar radiation is essential for grid balancing, real-time unit dispatching, scheduling and trading in the solar energy utilization system. In order to provide reliable forecasts of solar radiation, a novel hybrid model is proposed in this study. The hybrid model consists of two modules: a mesoscale numerical weather prediction model (WRF: Weather Research and Forecasting) and Kalman filter. However, the Kalman filter is less likely to predict sudden changes in the forecasting errors. To address this shortcoming, we develop a new framework to implement a Kalman filter based on the clearness index. The performance of this hybrid model is evaluated using a one-year dataset of solar radiation taken from a photovoltaic plant located at Maizuru, Japan and Qinghai, China, respectively. The numerical results reveal that the proposed hybrid model performs much better in comparison with the WRF-alone forecasts under different sky conditions. In particular, in the case of clear sky conditions, the hybrid model can improve the forecasting accuracy by 95.7% and 90.9% in mean bias error (MBE), and 42.2% and 26.8% in root mean square error (RMSE) for Maizuru and Qinghai sites, respectively