## 1 Introduction  
The primary purpose of the power system is to meet the demand for electricity. The ongoing advancement of the power grid has led to increasingly intricate fluctuations in power load [1], rendering research on power load forecasting a pivotal component of grid management. Load forecasting provides a clear understanding of future demand levels and patterns, forming the basis for the planning and decision-making process. By forecasting future electricity demand, power companies can reasonably plan the generation, transmission, and distribution facilities to avoid power shortages and reduce power outages. Load forecasting also helps optimize resource allocation, reduce operating costs, and improve overall system efficiency. It can be said that load forecasting is an indispensable part of power system planning and scheduling, as it relates to the stability and sustainability of power supply, and has significant impacts on economic and social development [2]. Therefore, load forecasting has always been a critical issue in the field of electricity. Load forecasting is classified into traditional classic forecasting methods and intelligent forecasting methods. Initially, traditional classic forecasting methods based on statistical theory were widely utilized, primarily encompassing time series analysis [3] and regression analysis [4], among others. These methods heavily depend on historical data and mathematical models for prediction. While they feature simplified models, low computational complexity, and rapid prediction speed, their lack of robustness makesthem susceptible to random disruptions, resulting in reduced reliability when employed for intricate regional load forecasting. With the advancement of intelligent algorithms, some electricity load forecasting methods based on these algorithms have emerged. These algorithms comprise support vector machines (SVMs) [5], long short-term memory (LSTM) neural networks [6], gated recurrent units (GRUs) [7], and convolutional neural networks (CNNs) [8], which are adept at handling the nonlinear relationships and complex features of the load. These methods can effectively handle nonlinearity and multidimensional data, but they are sensitive to outliers, and facing data mutations or breakpoints can lead to performance degradation [9]. However, the high complexity of the load sequence limits the accuracy of load prediction using a single algorithm. In recent years, signal decomposition techniques such as wavelet transform [10] and Empirical Mode Decomposition (EMD) [11] have been widely applied in the field of load forecasting. These techniques decompose the load sequence into multiple components and individually predict each component to enhance feature extraction and prediction accuracy of the load sequence. In reference [12], a short-term load forecasting model based on EMD is established. EMD is used to decompose load data into a series of local features with different frequencies and time scales. The components are then predicted using a GRU and superimposed to obtain the final load forecast. In reference [13], a load decomposition using Variational Mode Decomposition (VMD) is utilized to obtain components with different frequencies, featuring smooth and approximately sinusoidal waveforms. The components are predicted and superimposed using an LSTM model, achieving effective load prediction and avoiding the mode-mixing phenomenon caused by EMD. Reference [14] proposes a prediction method through dual decomposition. It employs Complete Ensemble Empirical Mode Decomposition with Adaptive Noise (CEEM-DAN) to decompose load data, generating multiple Intrinsic Mode Functions (IMFs). It then utilizes VMD to perform a secondary decomposition on the IMF with the maximum sample entropy. A Support Vector Regression (SVR) model is established to predict each decomposed subsequence separately, and these subsequences are then superimposed to obtain the final load forecast.