## 1 Introduction  
Load forecasting plays an important role in power systems. Power grid planning, power scheduling, equipment maintenance, and so on all need to be carried out according to load forecasting. As an important reference basis for the power generation sector, short-term load forecasting plays an important role in reasonably arranging power generation tasks, automatic power generation control, and realizing real-time power supply and demand balance. Power load forecasting is a multidimensional and multilevel complex system that includes time, space, and attributes. Moreover, there are many random factors that influence the load, and they are highly nonlinear. Accurate load forecasting plays an important role in optimizing the economic dispatch and power market transactions and ensuring the smooth operation of power systems. With the deepening of load forecasting research, the forecasting objects are more diversified, the requirements of forecasting precision are higher, and the development of new theory and technology is more urgent [1; 2; 3]. Many international experts and scholars have performed large amounts of research work on short-term load forecasting theory and forecasting methods and have made significant, effective progress. The traditional method of load forecasting is based on mathematics, which has the characteristics of simple principles and fast computation speed and has made great contributions in the prediction study, among which Holt-Winters exponential smoothing (ES) and autoregressive integrated moving average (ARIMA) are the most representative. However, in the face of multifarious characteristic variables, dealing with huge data is a serious challenge. With the rapid development of artificial intelligence, great success has been achieved in the algorithm field of machine learning, such as artificial neural networks (ANN), support vector regression (SVR), and so on. The intelligent algorithm has a high fault tolerance rate for sample data and can process complex data with a high accuracy of prediction [3; 4; 5; 6; 7; 8]. Authors of a past paper [9] presented a technique for the bootstrap aggregation of ES methods, which use a Box-Cox transformation followed by a seasonal trend decomposition based on loess (STL) decomposition to separate the time series and recombined. This new method makes ES performs well and achieves excellent results. In a past paper [10], a univariate model for short-term load forecasting based on linear regression and patterns of daily cycles of load time series was proposed and compared to the performance of several regression methods in the model. Many researches have accomplished the implementation of artificial neural network (ANNs) for the electrical load forecasting [11; 12; 13]. Deep neural network (DNN) is a significant method developed based on ANN and its deep structure increases the feature abstraction capability of neural networks. A past paper [14] proposed DNN-based load forecasting models and applied them to a demand side empirical load database. In a previous work [15], an accurate deep neural network algorithm for short-term load forecasting (STLF) was introduced, displaying very high forecasting accuracy. Long short-term memory (LSTM) is also a variation of ANN which performs well on load forecasting. A past paper [16] proposed a LSTM-based neural network model for aggregating demand side load forecast over short- and medium-term monthly horizons. Probabilistic load forecasting (PLF) proved to be an effective forecasting method [17]. A past paper [18] introduced the basic definitions and benchmark models of probabilistic forecasts, and reviewed recent advances separately for probabilistic forecasting of solar power and PLF, and comprehensively introduced the benchmark models and performance metrics of load forecasting. The authors of a past paper [19] proposed a method where factor analysis and similar-day thinking were combined into a prediction model for short-term load forecasting, which is also performing well.