# GCN-Informer: A Novel Framework for Mid-Term Photovoltaic Power Forecasting
## 4 Experiment and Analysis
### Implementation Details and Benchmark Model
Furthermore, the GCN-Informer model incorporates Informer, a popular architecture in deep learning, known for its exceptional performance in sequence modeling tasks. With the ability to capture long-term dependencies and learn from extensive historical data, Informer empowers the GCN-Informer model to understand complex temporal patterns inherent in power generation datasets. The utilization of Informer allows the GCN-Informer model to outperform other models that lack this advanced architecture. The reasons why the GCN-Informer model achieves better performance than standard models like Transformer or Autoformer can be summarized as follows:  
(1) Dynamic relationship modeling: Standard GCNs use static adjacency matrices to represent spatial relationships between nodes. However, in many applications, the relationships between nodes may be dynamically changing, especially when dealing with time-series data. By using the product of query and key from the Informer attention mechanism to construct the adjacency matrix, GCN-Informer is able to capture the dynamic relationships between nodes that change over time, thereby better adapting to the characteristics of the data. (2) Combination of spatiotemporal features: The Transformer model captures temporal dependencies in sequences through its self-attention mechanism, but it does not directly consider spatial structural information. By combining the self-attention mechanism with GCN, GCN-Informer is able to consider both temporal and spatial features simultaneously. Specifically, the product of query and key reflects the importance of different time steps, and incorporating this information into the adjacency matrix of GCN enables the model to perform targeted feature aggregation in the spatial dimension as well. Certainly, after demonstrating the excellent predictive capabilities of GCN-Informer, it is crucial to pay attention to the efficiency of our model. As the GCN-Informer model and Preformer proposed in this article both incorporate the GCN structure, their specific time complexity cannot be represented by O notation. Similarly, the time complexity of LSTM and GRU also depends on the feature dimensions and hidden state dimensions. Therefore, we provide the running time for all models to predict one week of power generation using the dataset mentioned in the article (time refers to the required time for one epoch, with other parameters consistent with the article). Our graphics card is an NVIDIA GeForce RTX 4070 Ti. As shown in Table 4, although our proposed model is slower than other deep learning models, it is still within the same order of magnitude. If a NVIDIA GeForce RTX 4090 is used for calculation, the computation time should be faster. In summary, the GCN-Informer model's superior performance can be attributed to its ability to capture complex relationships, exploit Informer architectures for sequence modeling, and effectively leverage both spatial and temporal information. These advantages enable it to surpass traditional machine learning models in accurately predicting power generation. The findings of this study have significant implications for various industries, particularly those involved in energy production and management. By providing more accurate and reliable power generation predictions, advanced deep learning models like the GCN-Informer model can assist these industries in optimizing their operations and making informed decisions. \begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline  & **Ours** & **Informer** & **Autoformer** & **Preformer** & **Reformer** & **Transformer** & **FEDformer** & **LSTM** & **BP Neural** & **GRU** \\ \hline Complexity & - & \(O(nlogn)\) & \(O(nlogn)\) & - & \(O(nlogn)\) & \(O(n^{2})\) & \(O(n)\) & - & - & - \\ time (s) & 228.86 & 86.49 & 91.3 & 223.78 & 181.86 & 203.5 & 90.45 & 302.75 & 417.33 & 278.98 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: The computational complexity and running time for all models.