## 1 Introduction  
With the rapid development of China's economy, the power industry, which is transitioning from a monopoly business model to a competitive relationship, has put forward higher requirements for all departments of the power system [1; 2; 3]. The power sector conducts a comprehensive and detailed study of the data related to load prediction, formulates an efficient and economical power generation plan, and rationally arranges the output of the unit so that it can continuously provide users with safe and reliable power, meet the needs of each user, ensure the safety and stable operation of the power system, reduce power generation costs, and improve economic efficiency [4; 5]. Power system load prediction involves studying the change law of historical load data and finding out the relationship between load and various influencing factors, so as to predict future load. Load prediction can be generally be divided into the following four categories according to the length of the forecast: long-term, medium-term, short-term, and ultra-short-term. Long-term load prediction is mainly used to predict the load situation in the next several years, generally used for grid planning and reconstruction work [6; 7]; medium-term load prediction refers to prediction the load in the next few months to one year, mainly for reservoirs' operation scheduling, unit maintenance, and usage planning for fuel [8; 9; 10]; short-term load prediction is mainly for the next day to one week of load forecast, often used for optimizing the combination of water and thermal power and control of economic flow [11; 12; 13]; ultra-short-term load prediction is a hot spot of research in recent years [14; 15], and mainly refers to load prediction for the next few minutes. Usually used for power quality control, safety monitoring for online operation, prevention and emergency control, its prediction accuracy directly affects economic dispatch, online safety monitoring, automatic generation control (AGC) frequency modulation, and preventive control emergency situations [16]. Since the early 1960s, load prediction has begun to develop toward exploration, research, and application. At present, most researchers use some methods related to artificial neural network (ANN) and support vector machine (SVM) [17; 18; 19; 20]. Some scholars have proposed selecting a similar daily load to the input load, and then applying wavelet decomposition to decompose the load into low-frequency components and high-frequency components. Finally, a single neural network is used to predict the future load of these two components. Some scholars have used asymmetric quadratic loss functions to support vector regression to accurately predict the load, which effectively improves the accuracy of the power load prediction model. In the literature [21], a new hybrid intelligent algorithm based on Wavelet Transform (WT) and Fuzzy Adaptive Resonance Theory Maps (F-ARTMAP) is proposed. The prediction model has been proven by extensive comparison of predictions. In the literature [22], the empirical mode decomposition method is used to divide the time series into two parts, describing the energy consumption values of the trend and the local oscillation, respectively; these are then used for training the support vector regression model. In the literature [23], for problems applying the SVM to short-term load prediction, such as data preprocessing, kernel function construction and selection and parameter optimization, it does analysis, summarizes the existing solutions, and proposes the key issues to be solved in the next step. Some scholars have studied the electric load prediction problem in local areas [24] and proposed a new multi-model variable structure load prediction method based on adaptive clustering partitioning and support vector regression. Experiments show that this prediction method has higher precision and stronger robustness than the traditional neural network prediction method.