Figure 4 Bi-GRU structure
The Bi-GRU model·computational equation can be expressed as:
Where: and are the positive-order GRU model input weight matrix and time output weight matrix, respectively; and are the negative-order GRU model input weight matrix and time output weight matrix, respectively; and are the positive-order GRU model and the negative-order GRU model output weight matrix, respectively.

## TPE-CBiGRU Short-term Photovoltaic Power Prediction Model

1. Model Framework

This paper leverages the superior spatial feature extraction capabilities of Convolutional Neural Networks (CNN) and the long-term sequential feature extraction capabilities of Bidirectional Gated Recurrent Units (Bi-GRU). Additionally, it employs the Tree-structured Parzen Estimator (TPE)-based Bayesian optimization algorithm to fine-tune the parameters of the CBiGRU prediction model. The detailed structural framework is illustrated in Figure 5.