## 4 Conclusions  
This paper proposes a group resident daily load forecasting method fusing a self-attention mechanism based on load clustering to achieve accurate daily load forecasting. Firstly, a K-Shape-based clustering method is used for group resident loads. Because of the characteristics of high dimensionality and obvious differences in residential electricity consumption data, we use the K-shape algorithm to cluster the load data of group residents. It is divided into two types of typical residential electricity consumption curves: the high electricity consumption residential curve and the low electricity consumption residential curve. Therefore, in the subsequent prediction, the input vector similarity of the model is higher, and more accurate prediction results are obtained. Secondly, we use empirical mode decomposition to adaptively extract the IMF components and the residuals from the load data and reduce the noise by reducing the IMF component input. Finally, we propose an attention-based bidirectional neural network to predict residents' daily load. The self-attention mechanism is used to assign different weights to the time series information during prediction, which makes full use of the contextual information and the important information about the daily load of group residents.  
To summarize, the group resident daily load forecasting method proposed in this paper has an average accuracy rate of over 98%. At the same time, we also compared previous prediction methods using the same data set. The experimental results show that the method we proposed performs better than the previous methods in five evaluation met  
Figure 10: Performance comparison chart of different algorithms. (**a**) MAPE comparison. (**b**) MAE comparison. (**c**) MSE comparison. (**d**) SMAPE comparison.  
rics. Therefore, the group resident daily load forecasting method fusing the self-attention mechanism based on load clustering proposed in this paper can accurately predict the daily load of residents. This method can help power companies to accurately understand the individualized and differentiated needs of users. It can improve the accuracy and efficiency of electricity consumption forecasting. Meanwhile, it can assist power companies to expand their business and provide data support for power demand management.  
In future research, the method proposed in this paper can be used for short-term electricity load forecasting for other data sets. Other factors, including social factors and residential electricity consumption habits, can also be combined in the forecasting model. Other powerful artificial intelligence techniques can also be introduced into the hybrid model to further improve prediction accuracy.  
Conceptualization, J.C. and R.-X.Z.; Methodology, J.C.; Investigation, R.-X.Z. and Y.-B.Y.; Formal analysis, R.-X.Z.; Validation, R.-X.Z.; Resources, J.C.; Data curation, R.-X.Z. and C.-Q.L.; Writing--original draft, R.-X.Z. and C.-Q.L.; Writing--review and editing, C.-L.C., R.-X.Z. and Y.-B.Y.; Software, C.-Q.L.; Visualization, C.-Q.L.; Supervision, C.-L.C.; Project administration, C.-L.C. All authors have read and agreed to the published version of the manuscript.  
This work was supported by the Science and Technology Development Plan projects of Jilin Province, No. 20210201134 GX.  
This study is based entirely on theoretical basic research. It does not involve humans.  
The data used to support the findings of this study are available from the corresponding author upon request.  
The authors declare no conflict of interest.