# A Comprehensive Survey for Deep-Learning-Based
## 6 Conclusions and Future Work  
This paper surveys state-of-the-art image-data-based abnormality detection in smart grids and outlines several key enablers. Considering the intricacies of the applications, we have broadly divided this survey into two parts: image-data-based abnormality detection and related methodologies. Within each of these topics, we have mainly surveyed the various approaches that have been advanced for enabling smart grids to run intelligently. Nevertheless, considering that the applications of image data in abnormality detection are far from well established, there are quite a large number of issues that require in-depth investigation. For example, the deployment of equipment for collection of high-quality image data and solutions for all-day environments are expected to facilitate possible future research on multimodal image-data-based abnormality detection at long range.  
\begin{table}
\begin{tabular}{c c c} \hline \hline  & **Highlights** & **Disadvantages** & **Usage and Applicability** \\ \hline Classical ML & (1) Data-driven; & (1) High data/model error susceptibility; & (1) Commonly used in lightweight devices; \\ abnormality detection & (3) Performance may saturate with a large amount of data. & (2) Not robust to noisy data. & (1) Commonly used in \\ \hline DL-based abnormality detection & (1) Data-driven; & (1) Data and label hungry; & (1) Mostly used in \\ abnormality detection & (3) Data hungry; & (2) High storage and computation requirement; & well-equipped platforms; \\ \hline \hline \end{tabular}
\end{table}
Table 6: Comparison of ML- and DL-based abnormality detection.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Technique Used** & **References** & **Features** \\ \hline \multirow{2}{*}{Convolutional neural network (CNN)} & Supervised: [27; 33] & Ability to deal with large-scale data \\  & Semi-supervised: [20; 35] & \\ \hline \multirow{2}{*}{Recurrent neural network (RNN)} & Supervised: [19; 28; 29] & \\ Long short-term memory (LSTM) & Semi-supervised: [115; 84] & \\  & Unsupervised: [84; 85] & \\ \hline \multirow{2}{*}{AutoEncoder (AE)} & Semi-supervised: [21; 37] & \\  & Unsupervised: [22; 81; 83] & Simple, ability to learn from unlabeled data \\ \hline \multirow{2}{*}{Generative adversarial networks (GAN)} & Semi-supervised: [30; 38; 86] & \\  & Unsupervised: [87] & \\ \hline \hline \end{tabular}
\end{table}
Table 5: Deep-learning-based anomaly detection techniques.  
**Author Contributions:** Conceptualization, G.W., R.H., L.P., W.Y., L.C. and L.P.; Formal analysis, L.C. and L.P.; Funding acquisition, L.P., W.Y. and R.Q.; Investigation, G.W., R.H., L.P., W.Y., L.C. and L.P.; Methodology, F.Z., G.W., Y.M., R.H. and L.C.; Project administration, Y.M., H.G., R.H., L.P. and R.Q.; Resources, R.Q., L.P. and W.Y.; Supervision, R.Q., L.P. and W.Y.; Visualization, G.W., R.H., L.P. and W.Y.; Writing--original draft, All authors; Writing--review & editing, All authors. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the project No. YNKJXM20191246 and N.S.F.C 61873163.  
Not applicable.  
The authors thank the editor and reviewers for their useful comments, which have improved the quality of this paper. The authors acknowledge the funding supporting the project (No. YNKJXM20191246), which focuses on the construction of satellite remote-sensing technology for power applications and wide-area intelligent monitoring of environments. The authors also thank members from the MSP group at Shanghai Jiao Tong University for their discussions and proofreading of this paper.  
The authors declare no conflict of interest.