Abstract
High-precision forecasting of short-term wind power (WP) is integral for wind farms, the safe dispatch of power systems, and the stable operation of the power grid. Currently, the data related to the operation and maintenance of wind farms mainly comes from the Supervisory Control and Data Acquisition (SCADA) systems, with certain information about the operating characteristics of wind turbines being readable in the SCADA data. In short-term WP forecasting, Long Short-Term Memory (LSTM) is a commonly used in-depth learning method. In the present study, an optimized LSTM based on the modified bald eagle search (MBES) algorithm was established to construct an MBES-LSTM model, a short-term WP forecasting model to make predictions, so as to address the problem that the selection of LSTM hyperparameters may affect the forecasting results. After preprocessing the WP data acquired by SCADA, the MBES-LSTM model was used to forecast the WP. The experimental results reveal that, compared with the PSO-RBF, PSO-SVM, LSTM, PSO-LSTM, and BES-LSTM forecasting models, the MBES-LSTM model could effectively improve the accuracy of WP forecasting for wind farms.