## 6 Conclusions  
The setting of electricity prices is closely related to the interests of each participant, and accurate electricity price prediction can help participants understand future trends; however, due to the interference of many influencing factors, prediction accuracy is not ideal, so how to improve the prediction accuracy of real-time electricity prices has become an important direction in current research. The proposed SAMD model can categorize a time-series dataset into quality clusters of high, medium, and low levels, which can be represented as different kinds of real-time electricity price intervals to predict the general trends of the prices. The experimental results were used to analyze and verify the advantageous model to predict the real-time electricity prices, and it is suitable for different types of real datasets.  
However, the method in this paper also has limitations: the feature quantization relies on human settings and lacks adaptive learning ability, which may lead to processing bias and then affect prediction. We plan to extend this work to develop a multidimensional data attribute calculation model for mining the influence of indirect factors on real-time electricity prices in our future work.  
Conceptualization, L.W., T.H.Z. and W.D.; formal analysis, T.H.Z., X.X. and G.H.; funding acquisition, L.W., T.H.Z. and L.K.; investigation, X.X. and L.K.; methodology, T.H.Z., X.X. and G.H.; software, G.H. and W.D.; validation, T.H.Z., X.X. and G.H.; resources, L.W., T.H.Z. and L.K.; data curation, G.H. and X.X.; writing--original draft, T.H.Z., X.X. and G.H.; writing--review and editing, T.H.Z., X.X. and L.W.; visualization, X.X. and G.H.; supervision, [PERSON] and L.K. All authors have read and agreed to the published version of the manuscript.  
This work was supported by the Science and Technology Development Plan of Jilin Province, China (Grant No. 20220402033 GH), by the National Natural Science Foundation of China (No. 62406156), and by the Shandong Province Natural Science Foundation (No. ZR2023 QF036).  
Not applicable.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Time** & **SAMD-SVM** & **Neural Network** \\ \hline
01:00 & 0.323 & 0.209 \\
02:00 & 0.294 & 0.262 \\
03:00 & 0.452 & 0.327 \\
04:00 & 0.486 & 0.316 \\
05:00 & 0.414 & 0.312 \\
06:00 & 0.512 & 0.184 \\
07:00 & 0.394 & 0.322 \\
08:00 & 0.792 & 0.266 \\
09:00 & 0.242 & 0.215 \\
10:00 & 0.196 & 0.231 \\
11:00 & 0.438 & 0.342 \\
12:00 & 0.493 & 0.286 \\ \hline Avoid costs & 0.419 & 0.272 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Avoided electricity costs comparison.  
Data Availability Statement: A publicly available dataset was analyzed in this study. The PJM April 2018 dataset can be found here: [[https://dataminer2.pjm.com/feed/rt_hrl_lmps/definition](https://dataminer2.pjm.com/feed/rt_hrl_lmps/definition)]([https://dataminer2.pjm.com/feed/rt_hrl_lmps/definition](https://dataminer2.pjm.com/feed/rt_hrl_lmps/definition)) (accessed on 14 November 2024).  
The authors would like to thank the reviewers for their essential suggestions in helping improve this manuscript.  
Author [PERSON] was employed by Qingdao Integrated Service Group for Talented Individuals and Enterprises. The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.