LSTM Prediction Model based on Modified Bald Eagle Search for Offshore Wind Power

With the rapid development of new energy generation technology, a large number of wind distributed power supply into the power system. Accurate power load forecasting can effectively improve the consumption of wind power generation and realize the economic operation of the system. However, as the structure of the power system becomes more and more complex, it is difficult for traditional load forecasting methods to realize accurate prediction. In order to further improve the load prediction accuracy, this paper proposes a Long Short-Term Memory (LSTM) prediction model based on Modified Bald Eagle Search (MBES) algorithm. First, the LSTM prediction model is constructed and the fitness of the model is calculated based on the initialized parameters. Then, the parameters of the LSTM load prediction model are optimized by the proposed MBES. Finally, the load prediction is carried out under the optimal location parameters. Simulation examples show that the proposed model and algorithm have higher prediction accuracy.