Short-term Prediction of Wind and Photovoltaic Power Based on the Fusion of Attention Mechanism and Improved Whale Optimization Algorithm - Zou, Jin; Ye, Jiaqing; Xue, Zhiliang; Zhang, Li; Wu, Feiyun (Journal of physics. Conference series, 2025)
With the aim of enhancing the accuracy of predictions and stability of wind and solar power generation prediction models in different scenarios, a deep learning hybrid prediction model integrating the attention mechanism and the improved whale optimization algorithm is proposed. Firstly, Pearson correlation analysis is employed to measure the association between each feature and wind and solar power in order to select the meteorological features with stronger correlation. Secondly, given the limitations of traditional long short-term memory (LSTM) and Gate Recurrent Unit (GRU) models in power prediction, the LSTM-GRU hybrid prediction model is adopted for accurate short-term power prediction of wind and solar power. Then, a hybrid prediction model integrating the Attention mechanism (Attention) and the improved whale algorithm (IWOA) is put forward to enhance accuracy of wind and solar energy forecasts; Finally, the historical data of a certain new energy base in northwest China was taken as the experimental data, and the Attention-IWOA-LSTM-GRU model was used for prediction. The outcomes of the simulation indicate that, compared with the prediction effects of other models in the circumstances involving abrupt fluctuations in wind velocity and light intensity, the prediction accuracy of the Attention-IWOA-LSTM-GRU model is higher.