Wind Speed Time Series Prediction Model Based on EMD_LSTM_GWO

Time series data are crucial in various fields, capturing variable values over time and reflecting dynamic characteristics. Analyzing these data reveals hidden trends, periodicity, and randomness, enabling predictions of future changes, wind speed, a key factor in wind energy utilization, directly impacts wind power generation efficiency and stability. Correctly forecasting wind velocity is crucial for enhancing the efficiency of wind energy facilities, enhancing wind energy utilization, and ensuring power system stability. The present study presents a model for forecasting wind velocity that integrates the Empirical Mode Decomposition method, the Long Short-Term Memory neural network, and the optimization technique of the Grey Wolf algorithm. The model decomposes wind speed data using EMD, models and predicts components with LSTM, and optimizes LSTM parameters with GWO. Experimental results show the model's high accuracy and stability, significantly improving traditional methods.