Ultra-short-Term Photovoltaic Power Prediction Based on CNN-GRU-BiLSTM-AM

Abstract:

To improve the ultra-short-term prediction accuracy of photovoltaic power, a hybrid prediction model based on CNN-GRU-BiLSTM-AM was proposed. First, to improve data quality, outliers were processed, and the Spearman' s rank correlation coefficient and grey relational analysis were used to extract the key features affecting photovoltaic power. Second, to obtain the spatiotemporal features of photovoltaic power data, the local features in the spatial and temporal dimensions were respectively extracted by CNN and GRU, and the long-term dependence relationships in the time series were captured BiLSTM. Third, to obtain the important information of key historical time points, the Attention Mechanism (AM) was introduced, and the CNN-GRU-BiLSTM-AM prediction model was constructed. Finally, comparative experiments were conducted using a publicly available photovoltaic power dataset. The results show that the prediction model constructed in this study has a coefficient of determination of 99.1%, a root mean square error (RMSE) of 0.032 5, and a mean absolute error (MAE) of 0.026 6, indicating that the method effectively improves the prediction accuracy of photovoltaic power.