## 2 Background  
### Basic Methodology and Shortcomings of Existing Studies  
In recent years, scholars have proposed various methods to forecast the global electricity load. Before conducting power load forecasting, a common method used to process data is data reconstruction. Scholars at home and abroad have developed various decomposition algorithms for power load time series, such as wavelet decomposition [5], wavelet packet transform [6], empirical mode decomposition (EMD) [7; 8], and ensemble empirical mode decomposition (EEMD) [9], etc. Data reconstruction is utilized to reduce the impact of intermittency and randomness in the original data on the prediction results. Most of these decomposition algorithms decompose the original data and then apply neural network models to predict the subcomponents, which in turn improves the accuracy of the prediction. There are three types of methods for power load forecasting. They are time series analysis methods, statistical methods and machine-learning methods. Time series analysis methods mainly include the exponential smoothing modeling method [10] and Fourier expansion model [11]. This method considers the trend, seasonality, periodicity, and randomness of historical load data when performing load forecasting, but its modeling ability for nonlinear relationships is limited. Statistical methods refer to the analysis and modeling of historical load data to predict future power loads, mainly including the vector autoregressive model [12], multiple linear regression model [13] and uncertainty analysis theory [14]. However, this method not only has limited modeling ability for complex nonlinear relationships but also has limited modeling ability for external factors such as weather changes, holidays, etc., which may affect the accuracy of the load forecasting. Machine-learning analytics mainly include grayscale projection and random forest algorithms [15], deep belief network prediction [16], multi-core support vector machine algorithms [17], and LSTM models [18]. This method considers more complex patterns and relationships in power load forecasting, and it can handle nonlinear relationships, large-scale data, and multidimensional features. But it is impossible to extract valid information between discontinuous data. Deep learning (DL) is an important branch of machine learning that has received much attention in recent years and is widely applied in different engineering fields. Deep-learning models perform well in learning feature representations and simulating complex data relationships, effectively predicting complex associations hidden in big data [19]. The DL models include convolutional neural networks (CNNs), recurrent neural networks (RNNs) and fully connected neural networks (FCNNs). An FCNN is where each neuron in each layer is connected to each neuron in the next layer. It is suitable for structured data (e.g., tabular data) and simple classification or regression tasks. However, it has a large number of parameters and is prone to overfitting. It does not perform well for high-dimensional data (e.g., images, time series). A CNN uses convolutional and pooling layers to extract local features. It is suitable for processing image and video data. However, it has a relatively complex structure and requires more computational resources. It does not perform well for time series data or natural language-processing tasks. Thus, it is not very good at learning from sublinear data compared to an RNN. Among them, the RNN greatly improves the prediction compared to the FCNN and CNN because the RNN takes into account the temporal dependencies between sequential data through its unique self-cycling structure. In addition, long short-term memory (LSTM), as a significant representative of RNNs, has been widely used in problems such as load forecasting.