## 1 Introduction  
Power load forecasting is an important area of research within the field of power systems. The field has received increasing academic attention due to its critical role in the efficient and economical operation of power systems [1]. Electrical load forecasting can be categorized into three categories based on the forecasting horizon [2]: Short-Term Load Forecasting (STLF), Medium-Term Load Forecasting (MTLF), and Long-Term Load Forecasting (LTLF). STLF, which ranges from one hour to one week, is significantly influenced by weather conditions and recent load data. These forecasts play a crucial role in real-time grid management. MTLF, typically spanning from one week to several months, is influenced by factors such as seasonal variations in electricity consumption, holidays, and working days. These forecasts are essential for maintenance scheduling and fuel reserve management. LTLF, which extends beyond one year, is influenced by factors such as demographic changes, economic growth, and energy policies. These forecasts are crucial for system planning and optimization. This paper focuses on STLF. The proposed model leverages deep learning techniques, specifically integrating the Stochastic Configuration Network (SCN), to achieve high accuracy in load forecasting results. With the growth in global electricity demand, improving the accuracy of load forecasting has become increasingly critical [3]. Many factors affect electricity load, such as regional differences, socio-economic activities, weather, and prices [4]. Therefore, power load data are characterized by randomness, volatility, periodicity, and diversity. Extracting the intrinsic patterns of load change from historical power load data and developing an accurate forecasting method are key to successful load forecasting [5]. Various power load forecasting methods have been proposed to address specific challenges, each offering unique advantages based on different technologies, algorithms, or data types. Currently, power load forecasting methods are mainly divided into four categories [6]: statistical methods, artificial intelligence techniques, knowledge-based expert systems, and hybrid approaches. Statistical methods require the construction of explicit data models to represent the relationship between the electric load and contributing factors. Classical statistical methods include multiple regression analysis, exponential smoothing, and stochastic time series, among others. [PERSON] [7] proposed an adaptive load forecasting method based on the Generalized Additive Model (GAM) and big data estimation techniques. [PERSON] et al. [8] introduced a very short-term bus load forecasting model that utilizes Phase Space Reconstruction (PSR) and a Deep Belief Network (DBN). [PERSON] et al. [9] aimed to establish a national energy consumption forecasting framework using open-access data from the European Network of Transmission System Operators for Electricity (ENTSO-E). To construct the forecast density, they employed Gradient Boosting Regression Trees (GBRTs) and conducted benchmarking based on actual load data and forecasts provided by each country. [PERSON] et al. [10] extended the Generalized Additive Model (GAM) to GAM2, where a second GAM is applied to the squared residuals. Many statistical methods rely on linear models, which limit their ability to handle nonlinear relationships and complex patterns. Therefore, the nonlinear characteristics of complex electric loads cannot be precisely characterized using traditional methods [11]. Artificial intelligence methods used in power load forecasting include Artificial Neural Networks (ANNs), fuzzy logic, neuro-fuzzy systems, and Support Vector Machines (SVMs) [12; 13; 14]. [PERSON] et al. [15] leveraged Convolutional Neural Networks (CNNs) to optimize neural network hyperparameters for power load forecasting. [PERSON] et al. [16] proposed a power load forecasting model based on the Sparrow Search Algorithm (SSA), Variational Mode Decomposition (VMD), attention mechanism, and Long Short-Term Memory (LSTM).