# MFMNet: Multi-Scale Feature Attention Mixture Network for Short-Term Load Forecasting
## 3 Results
### Evaluating on AEP Dataset  
In order to verify the effectiveness of this method, this paper compares various load forecasting methods, including linear regression (LR), ARMA, BP neural network, SVR, decision tree (DT), CNN, LSTM, Resnet, STLF [12], ELM [14], CN-Fuzzy [22], MB-LSTM [28], MIFnet [30], and Transformer [37]. The above methods include the classic forecasting model and the SOTA load forecasting model recently proposed. According to the characteristics of the AEP dataset, 23 time series features, such as energy use, T1-T9, RH1-RH9, the temperature outside, pressure, humidity outside, and wind speed were used as the input of the network, and the total predicted load consumption was used as the output. In the comparative experiment, we used the grid search method to fine-tune the hyperparameters of each related method. In addition, some related methods disclosed their optimal hyperparameter settings, and we borrowed the optimal hyperparameter settings of these methods. Due to the large number of input feature dimensions, the importance of the feature is inconsistent. For example, we found that the temperature feature has a greater impact on the results, and the visibility feature has a smaller impact on the prediction results. Therefore, the assignment of the importance of the feature is particularly important for the accuracy of the prediction results. Table 1 shows the comparison results between this method and the other 14 methods. These 14 methods include the regression method, traditional machine learning method, and the latest deep learning method for load forecasting. From the four indicators, the method based on deep learning is obviously superior to the regression method and traditional machine learning method. Traditional machine learning methods are prone to over-fitting when facing large sample data, resulting in poor generalization effect, which affects the prediction accuracy. The regression-based method is the worst in MSE and MAE indicators, but it performs better in MAPE indicators. With the two methods, LR and ARMA, the error is large in the area with large load consumption, but the area with load consumption that is close to 0 performs stably, which lowers the overall mean absolute percentage error. For load forecasting, slight fluctuations in the forecast near the load usage of 0 will lead to a sharp increase in error indicators, but users will not be sensitive to these fluctuations. In general, the accurate prediction of load peaks will be beneficial to users, which is also the role of load forecasting. It can be seen from the results that although the prediction accuracy of the CNN method and LSTM method is better than the traditional machine learning model, the overall accuracy is not high, lower than the improved models CNN-Fuzzy, MB-LSTM and MIFnet. In addition, the Transformer network can effectively process time series data, and the overall performance is due to the existing methods. In order to show the effectiveness of this method more clearly, we conducted ablation experiments. The results show that the MCM structure and DAM module can effectively improve the accuracy of prediction. Figure 5 shows the comparison between this method and some deep learning methods. Because the prediction effect of the regression method and traditional machine learning method is not good, these methods are not drawn in the comparison chart. It can be seen from Figure 5 that the method in this paper fits the load curve very well and has the best effect. This is because this method can achieve multi-scale feature extraction, and can use the channel attention mechanism to assign the importance of 23 features, improving the effectiveness and accuracy of prediction. The LSTM method can effectively extract the temporal correlation, but from the experimental results, it is easy to have a prediction peak, i.e., it is easy to have a higher load prediction at the actual load peak, and the algorithm prediction inertia is large. The prediction accuracy of the CNN method at load change is not high.