## 1 Introduction  
Short-term load forecasting is the guarantee for the safe and economic operation of the power system and the basis for scheduling power supply plans [1]. By using analytical tools, short-term load forecasting explores the relationship and patterns between the historical loads and influencing factors and predicts the load size for the next few hours or days [2]. Deep learning methods have been proven to be effective. In recent years, deep recurrent networks (RNN), long short-term memory networks (LSTM), and gated recurrent units (GRU) have been widely used in short-term load forecasting due to their unique recurrent units. The work in [3, 4] utilized LSTM for short-term load forecasting, improving the prediction accuracy. Further work [5, 6, 7] employed a GRU as the underlying model for load forecasting, validating its effectiveness. One study [8] combined the Li-Sheng network, grey wolf optimization algorithm, and LSTM for load forecasting, ensuring the prediction accuracy while enhancing the algorithm efficiency. The studies in [9, 10] adopted bidirectional long short-term memory networks (BILSTM) for load forecasting, demonstrating that the BILSTM had better expressive power for continuous time series compared to the LSTM. With the continuous promotion of Internet of Things (IoT) technology and the extensive deployment of smart detection devices, such as intelligent temperature and humidity sensors and smart meters, a high-quality data foundation has been provided for the user-side power load forecasting based on deep learning [11]. The massive input data will increase the burden on the model and reduce the learning efficiency. Feature selection is an effective method to solve this problem by eliminating the irrelevant variables [12]. The filter method evaluates the importance of features using measurement algorithms independent of the prediction model, and it is widely applied in load forecasting, including the correlation coefficient [13], the gray correlation degree [14], mutual information [15], etc. Currently, the feature selection methods in the field of load forecasting generally directly measure the overall correlation between the feature sequence and the load sequence, while ignoring the different degrees of influence of the various factors on the load at different times. The impact of the temperature on the load variation varies significantly at different times of the day [16]. However, current prediction methods cannot dynamically track the fluctuations in the influence of input features over time, which in turn affects the prediction accuracy. Therefore, it is necessary to measure the degree of influence of the input features at different times and adjust them to improve the prediction accuracy. References [17; 18] used the Pearson correlation coefficient (PCC) method to measure the importance of input features, but the PCC can only reflect a linear correlation. References [19; 20; 21] used mutual information to measure the feature importance for feature selection, but they did not quantify the different degrees of influence of each feature, and mutual information requires extracting the joint distribution function of input variables, which is computationally complex. Currently, there are two main methods for net load forecasting with the integration of distributed renewable energy. One approach directly predicts the net load based on the load variation patterns, using methods similar to traditional load forecasting, often employing deep learning techniques [22]. However, these methods lack consideration for the impact of the photovoltaic (PV) output. The other approach involves decoupling the net load into the distributed PV output and actual load components. Each component is predicted separately and then added together to obtain the net load forecast. In reference [23], a method based on optimizing the pairing of net load curves was proposed. Reference [24] separated PV generation from the net load by repeatedly evaluating the decomposition residuals.