# GCN-Informer: A Novel Framework for Mid-Term Photovoltaic Power Forecasting
###### Abstract
The depth of the network leads to high computational complexity and time consumption, making it difficult to meet real-time prediction requirements. (2) How can we utilize GCN to more accurately extract hidden feature information among different features? Not only do we need to focus on the surface data of features but also we need to capture the relationship of mutual influence among data. As a deep learning model, the Transformer has successfully tackled natural language processing (NLP) and computer vision (CV) tasks, with its core idea being the self-attention mechanism. Self-attention [26], which allows the model to focus on the importance of different positions within a sequence when processing sequential data, has become a crucial component of the Transformer model. This mechanism computes the correlation scores between each element in the sequence and all other elements, enabling the model to dynamically attend to relevant parts of the input sequence and thus improving its ability to capture information. Furthermore, the self-attention mechanism can capture long-range dependencies within the sequence by directly computing the correlation scores between elements, which is difficult to achieve with traditional recurrent neural networks and convolutional neural networks. However, self-attention also has some drawbacks. Firstly, its computational complexity increases with the length of the sequence, which can pose computational resource challenges when dealing with long sequences. Secondly, the self-attention mechanism ignores positional information in the sequence and requires additional positional encoding to compensate for this limitation. Finally, self-attention may over-focus on certain unimportant information, leading to a decline in model performance. However, due to its high time complexity and memory requirements, it cannot be directly applied to long time-series prediction. In response to these challenges, numerous scholars have contributed several methods to overcome these limitations, yielding promising results. [PERSON] et al. [27] proposed the Reformer model, introducing Locality Sensitive Hashing (LSH) to enhance the attention module, thereby reducing the time complexity. They also introduced reversible residual layers to trade computational efficiency for memory consumption. [PERSON] et al. [28] introduced the Longformer model, capable of handling lengthy texts efficiently. This model addresses the issues associated with long sequences. [PERSON] et al. [29] presented the Linformer model, which decomposes the self-attention matrix with singular values. They also proposed a novel self-attention mechanism that reduces the time complexity to linear. [PERSON] et al. [30] introduced the Informer model, which not only reduces time complexity but also optimizes the input and output of long sequences. This model has demonstrated outstanding performance across various large-scale datasets. As mentioned above, the Informer model is a relatively new approach for long time-series prediction, and its research still has significant limitations. To enhance the accuracy of photovoltaic power prediction, this study introduces a hybrid forecasting model. The performance of the GCN-Informer model is evaluated by comparing it with other prediction models. The main contributions of this research are as follows:  
(1) This paper represents the first creative integration of the attention module in GCN and Informer. Each node in GCN is associated with query vectors and keyword vectors related to other nodes. Edges represent the connection strength between nodes. The strength of these connections is calculated through the dot product of query and key,which can reflect the interaction level between nodes. This information can be used to build a graph where the edge weights are determined by attention scores.