## 1 Introduction
Initially, the SSA is used to optimize VMD parameters; then, LSTM is employed for load forecasting, and an attention mechanism is introduced to enhance the model. [PERSON] et al. [17] applied Bidirectional Long Short-Term Memory (BiLSTM) to power load forecasting and proposed combining bidirectional memory with advanced neural network architectures. [PERSON] et al. [18] applied a Pooling-Based Deep Recurrent Neural Network (PDRNN) to household load forecasting and suggested that adding more hidden layers to the neural network could improve forecasting performance. [PERSON] et al. [19] applied an improved deep residual network to power load forecasting to enhance prediction results. Although the above models show good performance on their respective datasets, they suffer from limited applicability and poor generalization ability [1]. Expert systems, a notable achievement in artificial intelligence, rely on rule-based logic to mimic the decision-making processes of domain experts. These systems are commonly used as decision support tools. [PERSON] et al. [20] applied expert system theory to analyze load transfer in regional power grids during transformer, busbar, and line faults, providing load transfer schemes. A study utilized a knowledge-based reasoning expert system to assist decision-makers in selecting the most appropriate load forecasting model for long-term planning in power systems [21]. Additionally, researchers have developed a rule-driven method that incorporates prior knowledge from experts on load curves, integrating this knowledge into statistical models to enhance forecasting accuracy [22]. These research outcomes demonstrate that expert systems play a significant role in power load forecasting, enhancing the credibility and precision of forecasting results by combining expert insights with statistical analysis. Electricity load data are characterized by temporal correlation, volatility, and uncertainty, with volatility leading to significant forecasting errors in models [23]. Statistical methods and traditional machine learning approaches often fail to account for these characteristics simultaneously, resulting in insufficient load forecasting accuracy, indicating room for further improvement [24]. A single method to deal with power load data will result in low computational efficiency, high computational complexity, and high error rates. Over the years, numerous scholars have developed hybrid load forecasting models with the goal of achieving higher forecasting accuracy and lower error rates. [PERSON] et al. [23] proposed a hybrid model based on the Improved Complete Ensemble Empirical Mode Decomposition with Adaptive Noise (ICEEMDAN), combined with CNN, BiLSTM, and Self-Attention (SA) techniques, along with Wavelet Domain Denoising (WDD) to enhance data smoothing, applied to household electricity load data. This model leverages the spatial feature extraction capabilities of the CNN, the bidirectional temporal sequence extraction capabilities of BiLSTM, and the ability of SA to focus on key historical time points to reduce information loss. [PERSON] et al. [25] proposed a hybrid model based on the CNN, Improved Chaotic Particle Swarm Optimization (ICPSO), and LSTM for electric load forecasting. CNNs are used for feature extraction, ICPSO is employed to optimize LSTM parameters, and LSTM is then used for load forecasting. [PERSON] et al. [26] proposed a forecasting model that combines the CNN and LSTM, where CNNs are used to identify and extract local features from time series data, which are then input into LSTM for STLF. [PERSON] et al. [27] proposed a hybrid load forecasting model integrating a Residual Neural Network (ResNet) and LSTM, using a ResNet for feature extraction and LSTM for STLF. [PERSON] et al. [28] proposed an improved ARIMA-LSTM model that allocates weights based on the forecasting errors of the two models in the training set. [PERSON] et al.