Figure 2 CNN architecture
2)Bi-GRU structure: The Bidirectional Gated Recurrent Unit (Bi-GRU) is a specialized variant of the standard GRU architecture, comprising two GRU layers with identical outputs but opposite directions of information flow. The Bi-GRU model simultaneously considers information from both past and future time steps, thereby fully exploiting the temporal characteristics of photovoltaic power data and enhancing data utilization and prediction accuracy [36, 37]. The structure of a single-layer GRU is illustrated in Figure 3.
In Equation (1), represents the input to the GRU model at time step ; and denote the sigmoid and hyperbolic tangent activation functions, respectively; is the reset gate, which indicates the influence of the input at time and the hidden state output from the previous time step on the candidate hidden state ; is the update gate, indicating the extent to which the hidden state output from the previous time step influences the hidden state output at time ; is the candidate hidden state, computed from the input and the hidden state output from the previous time step; is the hidden state output at time . The weight matrices ， and correspond to the reset gate, update gate, and candidate hidden state, respectively.