## 5 Conclusions  
Accurate electricity load predictions are crucial to determine the supply and demand relationship of electric energy. In order to guarantee the sensible and efficient use of electric energy, high-efficiency and high-accuracy electricity load forecasts may provide valuable information to the electric industry, suppliers, etc. In this study, we propose a novel model for forecasting electricity loads called ELFNet using a deep convolutional attention mechanism. The inherent features of the 2D image input are extracted by deep convolutional blocks from the electricity load time series input by GAF, which is then added after the convolutional structure. CA and SA modules are added after the convolutional structure and aim to further optimize the intrinsic features extracted by the convolutional block.  
The results show that the prediction accuracy is improved to varying degrees by the addition of CA and SA modules at prediction horizons of 1 h, 2 h, and 3 h, respectively, and the greatest improvement is achieved by adding CA and SA modules simultaneously. We evaluated the prediction performance of the network when different attention modules were added to the deep convolution. Additionally, when compared to the traditional deep learning networks ResNet-18, ResNeXt-50, and GoogLeNet, ELFNet also achieves the highest prediction accuracy at multiple prediction horizons. At the 1 h ahead forecasting, the RMSE value of ELFNet is 0.0316, which is higher by 44.38%, 38.42%, and 37.49% than the 0.0569 value obtained by ResNet-18, 0.0514 value obtained by ResNeXt-50, and 0.0506 value obtained by GoogLeNet. At the 2 h ahead forecasting, the RMSE value of ELFNet is 0.0346, which is 50.49%, 40.86%, and 25.27%, higher than the 0.07 value of ResNet-18, 0.0586 value of ResNeXt-50, and 0.0464 value of GoogLeNet. ELFNet's RMSE value for the 3 h ahead forecasting is 0.0417, which is higher than the numerical model of 0.0836 obtained by ResNet-18, 0.0661 obtained by ResNeXt-50, and 0.0541 obtained by GoogleNet, showing an improvement of 50.10%, 36.85%, and 22.85%, respectively. In addition to increasing prediction efficiency while maintaining accuracy, future work will be focused on optimizing the network structure of ELFNet, tweaking the network's hyperparameters, and modifying the network structure in accordance with the data's inherent features.  
Conceptualization, P.Z., G.L. and X.S.; Methodology, P.Z., G.L. and X.S.; Formal analysis, X.S.; Investigation, P.Z. and G.L.; Writing--original draft, P.Z. and X.S.; Writing--review & editing, G.L.; Visualization, X.S.; Supervision, G.L.; Funding acquisition, G.L. All authors have read and agreed to the published version of the manuscript.  
This work was partially supported by the National Natural Science Foundation of China under Grants 61503282 and 62073301, the Fundamental Research Funds for the Central Universities (WUT: 2021 III062 JC).  
The datasets generated and analyzed in the current study are available in the OpenDataElia repository, [[[https://opendata.elia.be/pages/home](https://opendata.elia.be/pages/home)]([https://opendata.elia.be/pages/home](https://opendata.elia.be/pages/home)) (accessed on 1 March 2022)].  
The authors declare no conflicts of interest.  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline
**Metric** & **Horizon** & **SNR20** & **SNR30** & **SNR40** & **SNR50** & **SNR60** & **SNR70** & **ELFNet** \\ \hline \multirow{2}{*}{RMSE} & 1 h & 0.03742 & 0.03317 & 0.03162 & 0.03317 & 0.03461 & 0.03606 & 0.0316 \\ \cline{2-10}  & 2 h & 0.05657 & 0.03873 & 0.03606 & 0.03873 & 0.03742 & 0.04 & 0.0346 \\ \cline{2-10}  & 3 h & 0.05916 & 0.05521 & 0.