Short-term wind power prediction framework using numerical weather predictions and residual convolutional long short-term memory attention network

As a prominent global source of renewable energy, wind power generation had been experiencing rapid growth. The more precise prediction of short-term wind power was essential to ensure the stable and cost-effective operation of power systems. In response, a wind power prediction framework using numerical weather predictions (NWPs) and Residual Convolutional Long Short-Term Memory Attention (Res-ConvLSTM-Attention) network was proposed in this study. Addressing the issue of significant errors in individual NPW, Weighted Naive Bayes (WNB) model and Multivariate Quadratic Nonlinear Regression (NR) model were employed to fuse the four NWPs wind speed and direction characteristics respectively, aiming to obtain more accurate weather forecast data. Given the difficulty in accurately predicting due to the randomness of wind power, a Res-ConvLSTM-Attention network was proposed for short-term wind power prediction. The Res-ConvLSTM unit extracted deep spatiotemporal features while effectively alleviating network degradation and gradient vanishing issues caused by network deepening. The Attention unit allocated higher weights to key features, and their combination enhanced the accuracy of wind power prediction. Finally, using the data provided by Challenge Data for experimental analysis, the results showed that the mean absolute error (MAE), root mean square error (RMSE), mean arctangent absolute percentage error (MAAPE) and coefficient of determination (R2) value were 0.0758, 0.1163, 0.4364 and 0.946, affirming the effectiveness of the wind power prediction framework.