MGRU-LSTM Combined Model for Wind Speed Prediction

Wind speed prediction is crucial for optimizing wind farm and power system operations. The magnitude, nonlinearity, and randomness of wind speed affect the stable operation of wind power systems. This paper proposes a wind speed combined prediction model based on a modified Gated Recurrent Unit (MGRU) and Long Short-Term Memory (LSTM). Firstly, an MGRU model is proposed by simplifying the GRU architecture into a single-gated structure, enhancing prediction accuracy while reducing computational complexity. Subsequently, the initial wind speed data and related features are fed into the MGRU and LSTM models respectively. Finally, the optimal weighted combination algorithm is utilized to determine the weight of the prediction results of the two models, and the final prediction value is obtained. The experimental results show that the MGRU-LSTM model has significantly improved the overall prediction performance with RMSE and MAPE of 0.6551, 0.0685, and time of 150.1s, respectively, which meets the wind speed prediction requirements of wind farms.