## 1 Introduction  
The rapid development of society has led to a significant rise in energy consumption and the depletion of traditional energy sources. This has created a growing demand for the efficient use of energy, particularly in our daily lives. The absence of a consistent pattern in power demand could lead to an imbalance between supply and demand, resulting in energy losses [1]. Electricity load forecasting plays a crucial role in informing long-term policy decisions aimed at addressing these challenges. It also provides valuable information to the electrical sector, suppliers, and market regulators [2]. Numerous researchers have proposed various forecasting approaches to enhance the accuracy of electricity load forecasting [3, 4]. These approaches can be broadly categorized into two groups. The first group comprises classic statistical models, including linear regression models [5, 6], autoregressive moving average models [7, 8], and autoregressive integrated moving average models [9]. Despite their long-standing use, speed of calculation, and practicality, traditional methods often struggle with accuracy and effectiveness when analyzing electrical load data that contain significant random elements and high levels of nonlinearity [10]. The second category of prediction techniques involves machine learning algorithms, such as artificial neural networks, support vector regression, decision trees, and XGBoost. With the increasing improvement in the available computational resources, deep learning (DL) has become a highly successful data-driven technology in the field of forecasting electricity load [11]. The ability of deep neural networks (DNNs) to forecast electricity loads with strong nonlinearity has been exploited [12]. [PERSON] et al. introduce a medium to short-term load forecasting (MTLF; STLF) model that can be used to forecast load at different times of the month and on different days [13]. However, DNNs have the drawback of not being able to simulate some delicate changes in time series, and the training process is vulnerable to gradient disappearance and easily causes the prediction results to reach the local optimum [14]. [PERSON] et al. propose an online adaptive RNN, which is a load forecasting method that can continuously learn from newly arrived data and adapt to new patterns [15]. [PERSON] et al. use RNN and ARIMA for load forecasting under concept drift [16]. [PERSON] et al. propose a multi-scale RNN model with short-term and long-term memory for load forecasting [17]. While RNNs struggle with long-term time dependencies and cannot effectively represent large time series, LSTMs have emerged as a solution to this issue [18]. [PERSON] et al. combine LSTM with the self-attention mechanism (SAM) to develop a hybrid model with two input channels [19]. [PERSON] et al. proposed a hybrid approach using Prophet and LSTM models to predict accurate loads. The Prophet model predicts the raw load data using both linear and nonlinear data, and the nonlinear data are trained using LSTM [20]. [PERSON] et al. proposed a new hybrid forecasting model for short-term power load and price forecasting. The proposed method consists of three modules: wavelet transform to eliminate the fluctuation behavior of the power load and price time series, feature selection based on entropy and mutual information, and finally LSTM to train the model [21]. The incorporation of gating units like forgetting gates and memory gates has significantly enhanced the ability of these models to address long-term time dependencies and spatial complexity. Historically, LSTM and its variations have been widely utilized in power load forecasting models. Due to the advantages of CNNs in processing image data with strong nonlinearity and the ability to extract more intrinsic data features, the convolutional neural network (CNN) was adopted to process electricity load data [22]. [PERSON] et al. proposed a novel STLF model based on 2D CNN.