# Short-Term Power Load Forecasting Using an Improved Model Integrating GCN and Transformer
## 2 Related Works  
Power load forecasting essentially involves using data analysis methods to identify the key factors that affect power load and construct models for prediction. Since the emergence of the power load forecasting task, a variety of methods have been used for this task. The methods commonly used for power load forecasting in the early days were mainly traditional methods. Due to the limitations of the equipment used for data collection and the power supply and demand relationship at that time, there was limited data and few influencing factors. Researchers often used traditional and simple methods, such as ARMA [19], ARIMA [20], and Exponential Smoothing [21], for prediction. These methods are simple to use and have low time complexity. However, with upgrades of data collection equipment and the complications related to power relationships, the traditional prediction methods are no longer applicable. Therefore, people have gradually introduced new methods, such as machine learning methods and wavelet transform. [PERSON] et al. proposed an innovative power load forecasting method [22]. This method ingeniously utilizes wavelet transform to decompose power data, then builds models for different component data, and finally fuses the results to obtain the final forecasting result. This method significantly improves the accuracy of power load forecasting. [PERSON] made improvements based on the support vector regression (SVM) model and optimized the model parameters by using the PSO method, which also enhanced the prediction effect [23]. The work conducted by [PERSON] is similar to that of [PERSON] [24]. Both used the PSO method to improve the SVM model to form a new power load forecasting method. However, the difference is that [PERSON] adopted the memory algorithm of the improved PSO to optimize the parameters of the SVM model. The experimental results prove that this method significantly improves prediction accuracy. The abovementioned methods have undoubtedly made considerable progress or performed well in the field of power load forecasting. However, it is rather difficult to achieve precise feature mining and modeling when using them with an increasing amount of power load data. Therefore, researchers have introduced deep learning methods. [PERSON] et al. and [PERSON] et al. both used Recurrent Neural Networks (RNNs) for power load forecasting [8; 9]. Their experimental results indicated that the use of RNNs could improve the forecasting accuracy. [PERSON] et al. compared the results of various methods, such as Long Short-Term Memory (LSTM), Gate Recurrent Unit (GRU), and RNNs in power load forecasting, and found that GRU performed the best [25]. [PERSON] et al. applied Transformer to power load forecasting [26]. The experimental results show that this method is superior to others. [PERSON] et al. made improvements based on Temporal Convolutional Networks (TCNs) and DenseNet and proposed the Densenet-iTCN model for power load forecasting [27]. The experimental results verified that this method was superior to the baseline model. [PERSON] et al. effectively captured the influence of exogenous factors and time steps on the peak value in power load forecasting by using dual attention [28]. [PERSON] et al. proposed an attention model that can adaptively select the characteristics of power load and explored the influence of time step size [29]. [PERSON] et al. added self-attention to enhance information transfer based on Convolutional Neural Networks (CNNs) and BiGRU, which is helpful for finding the relationships among multiple factors in the power load dataset [30]. These methods have demonstrated strong performance and achieved good prediction results. However, there are still deficiencies. Due to the failure to consider the internal differences within different time scales and the external long-term sequence relationships in the power load forecasting data, these methods are still not precise enough in the process of data learning and modeling.