A Short-Term Wind Power Forecasting Model Based on 3D Convolutional Neural Network–Gated Recurrent Unit

Abstract

Enhancing the accuracy of short-term wind power forecasting can be effectively achieved by considering the spatial–temporal correlation among neighboring wind turbines. In this study, we propose a short-term wind power forecasting model based on 3D CNN-GRU. First, the wind power data and meteorological data of 24 surrounding turbines around the target turbine are reconstructed into a three-dimensional matrix and inputted into the 3D CNN and GRU encoders to extract their spatial–temporal features. Then, the power predictions for different forecasting horizons are outputted through the GRU decoder and fully connected layers. Finally, experimental results on the SDWPT datasets show that our proposed model significantly improves the prediction accuracy compared to BPNN, GRU, and 1D CNN-GRU models. The results show that the 3D CNN-GRU model performs optimally. For a forecasting horizon of 10 min, the average reductions in RMSE and MAE on the validation set are about 10% and 11%, respectively, with an average improvement of about 1% in R. For a forecasting horizon of 120 min, the average reductions in RMSE and MAE on the validation set are about 6% and 8%, respectively, with an average improvement of about 14% in R.