## 1 Introduction  
The increasing complexity of modern power systems, driven by the integration of renewable energy and the growth of distributed microgrids, has introduced new challenges to grid operation and planning. As the energy supply becomes more dynamic and decentralized, ensuring the stability and efficiency of the power system requires accurate short-term load forecasting. Such forecasting not only supports real-time decision-making in power dispatch and grid management but also plays a crucial role in shaping electricity pricing strategies and achieving low-carbon objectives. To address these needs, a wide range of forecasting techniques have been developed, which generally fall into two main categories: traditional statistical models and data-driven approaches based on machine learning. Earlier studies primarily relied on traditional statistical models due to their simplicity and interpretability. For example, [PERSON] et al. used autoregressive integral moving average (ARIMA) for one-day-ahead load forecasting [1]. [PERSON] et al. used principal component regression (PCR) for medium-term load forecasting [2]. However, traditional statistical methods often fall short when it comes to forecasting nonlinear data like electricity load. Machinelearning methods excel at managing complex data, which makes them increasingly preferred for load forecasting. For example, [PERSON] et al. found that the Support Vector Machine performed better than statistical methods in predicting load data [3]. In recent years, deep learning has attracted considerable interest in the field of power load forecasting, owing to its capability to automatically learn complex temporal patterns from time series data without the need for manual feature engineering [4; 5; 6]. However, the variation of power load time series is affected by several factors, limiting the prediction accuracy of individual models. Therefore, more and more researchers tend to combine multiple methods into hybrid models to improve prediction accuracy. [PERSON] et al. designed a composite forecasting framework that leverages the strengths of convolutional neural network (CNN) and gated recurrent unit (GRU), which successfully enhanced the effectiveness of feature extraction from load data [7]. [PERSON] et al. combined CNN and long short-term memory network (LSTM) to extract feature information from load data, demonstrating better performance compared to other methods [8]. To capture features more efficiently, some studies have introduced Attention Mechanism into prediction models [9]. [PERSON] et al. proposed an attention-based LSTM network, where the feature-attention-based encoder can adaptively select the input features, and the decoder can mine the temporal dependencies, which improves prediction accuracy and generalization ability [10]. [PERSON] et al. proposed a CNN-BiGRU-Attention model for load forecasting, where the integration of the attention mechanism reduced the amount of forecasting errors [11]. However, these models still have certain limitations for complex load data. Therefore, data decomposition techniques are employed to handle complex electricity load data. By decomposing the time series into intrinsic modal functions (IMFs), these techniques help reduce the instability of the data, thereby facilitating feature extraction and enhancing forecasting accuracy. [PERSON] et al. combined empirical mode decomposition (EMD) and GRU for short-term load forecasting and demonstrated improved prediction accuracy [12]. However, the occurrence of intermittent fluctuations within the load data gives rise to modal aliasing in the extracted IMFs. Therefore, more advanced techniques have been applied in load forecasting. [PERSON] et al. used complementary ensemble empirical mode decomposition (CEEMD) to decompose the load sequence and introduced an evaluation factor to reconstruct the modal components for better forecasting performance [13]. [PERSON] et al.