# GCN-Informer: A Novel Framework for Mid-Term Photovoltaic Power Forecasting
## 3 Experimental Preparation and Framework
### Experimental Framework  
According to Figure 3, the photovoltaic power generation prediction model is based on the following framework: data preprocessing, data splitting, model training, and model scoring.  
**Data preprocessing:** The dataset exhibits some instances of missing values, prompting this study to employ interpolation techniques for their imputation. As illustrated in Figure 4, Pearson correlation analysis played a pivotal role in the feature selection process, enabling the removal of those features with weak correlations to the target prediction values. In the chart, if the absolute value of the Pearson coefficient between two features is less than 0.4, then we consider the correlation between these two features to be weak. Specifically, after observing the Pearson correlation coefficients between all the features and the prediction target, features displaying an absolute Pearson correlation coefficient below 0.4 were excluded from consideration, on the grounds of their limited relevance to the predictive target. Consequently, seven carefully chosen features were designated as inputs for the GCN-Informer model, with the actual power output serving as the target variable as showcased in Table 1. Following the comprehensive data cleaning procedures, an upsampling method was implemented to transform the original dataset, initially collected at 5 min intervals, into a dataset with 1 h intervals. During this consolidation process, feature values were aggregated using their respective mean values, while the predicted target variable was amalgamated through summation.  
**Data splitting:** When employing the hold-out method combined with temporal splitting, we initially divide the dataset into three temporally consecutive and non-overlapping parts: training, validation, and test sets. This ensures that the model does not have access to future information during training and evaluation, preventing data leakage issues.  
Specifically, we allocate 98% of the entire time series to the training set, which is used to train the model and learn the underlying patterns in the data. Of the remaining 2%, half (i.e., 1% of the entire dataset) is designated as the validation set. This set is utilized for tuning model parameters and hyperparameters during the training process and for obtaining initial performance estimates. The other half (also 1% of the dataset) serves as the test set, providing a final assessment of the model's performance and its ability to generalize to unseen data.  
**Model processing:** A comparison of the GCN-Informer performance with other models will be conducted in this experiment. To evaluate the model's performance, MAE and MSE are used in this paper. MAE represents the mean of the absolute error between the predicted value and the observed value. The MSE represents the mean of the sum of squares of the error corresponding to the predicted data and the original data. The results will be visualized, and the model performance will be better compared and evaluated:  
\[MAE=\frac{1}{\text{m}}\sum_{i=1}^{m}\|(y_{i}-\hat{y}_{i})\| \tag{11}\] \[MSE=\frac{1}{\text{m}}\sum_{i=1}^{m}\left(y_{i}-\hat{y}_{i}\right) ^{2} \tag{12}\]  
where \(y_{i}\) is the value of the original power generation, and \(\hat{y}_{i}\) is the value of the predicted power generation.