# GCN-Informer: A Novel Framework for Mid-Term Photovoltaic Power Forecasting
###### Abstract
[16] leveraged LSTM models for photovoltaic prediction in scenarios with limited data availability, showcasing the accuracy of LSTM models in photovoltaic forecasting. This study underscores the robustness of LSTM in scenarios where data scarcity is a challenge, making it a suitable choice for practical applications in photovoltaic power generation prediction. The accumulating evidence [17; 18] points to the accuracy and effectiveness of LSTM in photovoltaic power prediction. Its ability to capture temporal patterns and long-term dependencies, combined with additional meteorological data, makes LSTM a promising candidate for enhancing photovoltaic power generation forecasting. The memory unit of an LSTM (Long Short-Term Memory) comprises a cell state and three gates: the input gate, forget gate, and output gate. At each time step, the LSTM receives the input for the current time step and the hidden state from the previous time step, updating the cell state and hidden state through a gated mechanism. This structure allows the LSTM to selectively retain and update information, maintaining effective memory capabilities when processing long time sequences. By stacking multiple LSTM layers, the network can further extract and represent complex features in the time series. However, LSTMs still suffer from the limitations of having too many parameters, leading to overfitting and an inability to effectively handle very long sequence inputs. Currently, there has been a growing interest in utilizing Graph Convolutional Networks (GCNs) to address the spatio-temporal challenges in photovoltaic power prediction [19; 20; 21; 22; 23]. [PERSON] et al. [24] proposed a spatio-temporal graph neural network prediction method for regional photovoltaic electricity based on weather condition recognition. They employed Graph Convolutional Networks as the predictive model, enabling the capture of spatial features through graph convolutional models and temporal features through gated convolutional neural units. This approach resolves issues related to the inadequate consideration of time correlations in power output and physical connections between photovoltaic stations caused by cloud movement. [PERSON] et al. [25] introduced an optimal graph-structured approach that considers surrounding spatio-temporal correlations for short-term solar power forecasting. Based on complex network theory, they proposed a novel metric to evaluate graph structure connectivity, thereby enhancing the predictive capabilities of Graph Convolutional Network models. This method tackles the problem of information redundancy commonly encountered when modeling by indiscriminately using all station data without differentiation as seen in most existing approaches that consider spatial information from neighboring sites. While these two methods are innovative in addressing spatio-temporal challenges in photovoltaic power prediction, they do not fully resolve the issue of accurately accounting for sudden extreme weather events. In situations where unexpected weather phenomena, such as severe storms or rapid weather fluctuations, significantly impact photovoltaic power generation, these methods may still face challenges in providing precise predictions. The models primarily rely on historical data and may struggle to adapt to unanticipated, extreme weather conditions, which can lead to inaccuracies in power output forecasts. While the models we mentioned above have made significant advancements in handling time-series data, they face challenges when applied directly to mid-term photovoltaic power prediction due to the focus of this study on long time-series processing. This gives rise to two major challenges:  
(1) How do we address the computational challenges associated with LSTM models in parallel processing when applied to photovoltaic power prediction? Although LSTM models solve the problem of gradient vanishing during RNN backpropagation, they still face computational challenges in parallel processing.