# GCN-Informer: A Novel Framework for Mid-Term Photovoltaic Power Forecasting
###### Abstract
The age of big data has dawned, and artificial intelligence has permeated the foundational frameworks of various industries. Models employed for photovoltaic power generation forecasting can be broadly categorized into two types: deep learning models and non-deep learning models. A modeling and prediction framework is developed for photovoltaic power generation data in three regions, using a Random Forest (RF) algorithm optimized by Principal Component Analysis (PCA) and K-Means clustering. PCA and K-Means clustering [7] are employed to extract features that are similar to the prediction time points. The input data are then filtered to reduce interference. Additionally, an optimization algorithm is utilized to quickly select RF parameters, significantly mitigating errors caused by manual filtering factors. However, when addressing regression problems, RF does not provide continuous outputs, which can lead to overfitting when modeling data with certain noise patterns. To overcome this, a photovoltaic power output prediction model based on an empirical mode decomposition-support vector machine (EMD-SVM) is proposed [8]. Each decomposed component sequence and residual sequence is used to train respective Support Vector Machine (SVM) models for prediction. Furthermore, an artificial bee colony algorithm is employed to optimize the SVM model parameters. Finally, the outputs of each model are reconstructed to obtain the final prediction results. The predicted results demonstrate improved accuracy of the proposed model, and the computational speed of the optimized prediction model using the artificial bee colony algorithm is greatly enhanced. However, when dealing with large training samples, the quadratic programming algorithm in SVM consumes substantial machine memory and computation time, rendering it impractical for regression tasks. Deep learning models simulate the human thought process and are capable of performing complex computational tasks. They have emerged as a popular technique for photovoltaic power prediction. In the field of photovoltaic power generation prediction, recurrent neural networks (RNNs) have emerged as a leading AI model for accurate forecasting. As a specialized type of deep neural network, RNNs have also found widespread application in photovoltaic power generation prediction [9; 10; 11; 12]. Each recurrent layer of an RNN maintains a hidden state, which serves as input for the next recurrent layer and updates as the time series progresses. This structure enables RNNs to handle dependencies in time-series data and comprehend temporal information within the sequences. By harnessing this capability,RNNs can capture patterns and trends in photovoltaic power generation, achieving more accurate predictions than traditional machine learning algorithms. However, one of the most significant issues facing RNNs is the problem of vanishing gradients, which can limit the model's ability to learn complex patterns over extended time frames. This challenge becomes increasingly apparent as the length of the time series increases, potentially constraining the predictive accuracy of RNNs in long-term forecasting scenarios. In recent years, Long Short-Term Memory (LSTM) has emerged as a leading model for photovoltaic power prediction, addressing the temporal correlations between predicted and historical power that are often overlooked by non-deep learning models [13; 14]. The ability of LSTM to capture long-term dependencies in sequential data has made it a suitable choice for photovoltaic power generation forecasting. [PERSON] et al. [15] combined historical solar irradiance data and the sky forecast type with LSTM for photovoltaic generation forecasting, achieving promising results. This approach utilizes additional meteorological data to enhance the predictive accuracy, further highlighting the versatility of LSTM in photovoltaic forecasting. [PERSON] et al.