Wind power prediction method based on Attention-LSTM

Abstract Under the background of realizing the goal of “double carbon”, precise short-term wind power prediction can offer a scientific foundation for the effective integration of wind energy into the grid and the secure and steady functioning of the electricity system. However, the unpredictability of weather conditions causes wind power to be volatile, which makes prediction more difficult. Aiming at this problem, a short-term wind power prediction technique that combines LSTM neural network and attention is suggested, which can make the predictive model more effective in processing the inputs of relevant influencing factors. The outcomes demonstrate the higher accuracy of the Attention-LSTM prediction method., with the mean value of RSME of 6.12, the mean value of MAE of 4.51, and the mean value of MAPE of 18%, when the prediction model is trained with real data.