Abstract
A hybrid model combining self-attention temporal convolutional networks (SATCN) with bidirectional long short-term memory (BiLSTM) networks was developed to improve the accuracy of ultra-short-term photovoltaic (PV) power prediction. The self-attention mechanism and SATCN were used to extract temporal and correlation features, which were then linked to BiLSTM networks. The model's hyperparameters were optimized using the dung beetle optimization algorithm. The model was tested on a year-long dataset of PV power and outperformed convolutional neural networks, BiLSTM networks, temporal convolutional networks, and other hybrid models. It reduced the root-mean-square error (RMSE) by 33.1% compared to the other models. The model achieved a mean absolute error (MAE) of 0.175, a weighted mean absolute percentage error (wMAPE) of 4.821, and a coefficient of determination (R2) of 0.997. These results highlight the model's superior accuracy and its potential applications in solar energy development.