New energy power generation prediction based on PCA and machine learning methods

As the share of wind power generation in the global energy mix increases year by year, the volatility of wind power poses a challenge to grid stability. In order to improve the accuracy and stability of wind power prediction, this paper proposes a wind power prediction model based on PCA and BiLSTM. Firstly, the input data are dimensionality reduced by PCA to extract key features to reduce data complexity while retaining the main information of the data. Then, time series modelling is performed on the dimensionality reduced data using BiLSTM to capture the bidirectional time dependence of the data. Simulation results show that the model outperforms traditional methods in terms of prediction accuracy and stability, and RMSE and MAE are reduced, respectively. The method provides an efficient and reliable solution for wind power prediction, which contributes to the integrated control and scheduling of the power grid.