## 5 Conclusions  
To overcome data challenges, enhance technology portability, and prevent redundant investments, this study presents an LLM-based application framework integrating three key scenarios: load prediction and optimization, equipment anomaly detection with predictive maintenance, and power system security with data privacy. This framework shows promising applications for multi-source, heterogeneous, and massive data in modern power systems.  
Based on our LLM framework, we developed CLB-BER to address the core challenge of electricity consumption behavior analysis. The ECDW method optimizes the CLUBS algorithm's adjustment stage, enabling the accurate classification of consumption patterns and mapping between unlabeled behavior characteristics and user types. Our pre-trained BERT model predicts user types from new consumption data, enabling personalized strategies and intelligent load scheduling, overcoming the limitations of conventional single-analysis methods. By preserving temporal symmetry in consumption patterns, our approach achieves higher generalization across heterogeneous grid scenarios.  
Evaluations on real grid datasets demonstrate that our enhanced CLUBS algorithm significantly improves clustering precision. Compared to direct neural network training with raw data, our approach reduces computation time while increasing classification accuracy for new electricity data, providing valuable insights for targeted utility marketing strategies.  
This study has limitations worth noting. The experimental evaluation was conducted on electricity consumption data from a single local grid spanning only one year (2019). Thus, the generalizability of our approach across different geographic regions or extended time periods remains to be further validated. Future studies should aim to apply the proposed CLB-BER model to datasets from multiple power grids and diverse climatic regions, as well as perform longitudinal analyses over several consecutive years. More advanced LLM algorithms must be integrated for equipment fault diagnosis, predictive maintenance, and power system security. Future work will expand the system to accommodate various power system neighborhoods, addressing a wider range of practical challenges in the evolving energy landscape.  
Methodology, H.C.; Software, Y.Z.; Formal analysis, J.S. and N.Z.; Investigation, Y.Z.; Data curation, H.C.; Writing--original draft, Y.Z.; Writing--review & editing, N.Z.; Supervision, H.C.; Project administration, H.C. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Variant** & **SI** & **Accuracy (\%)** & **F1 (\%)** \\ \hline Full CLB-BER (Improved-C+DistilBERT) & 0.63 & 94.21 & 94.34 \\ \hline Without ECDW (CLUBS + DistilBERT) & 0.62 & 92.50 & 92.75 \\ \hline Improved-C+ SVM & - & 86.73 & 86.58 \\ \hline Improved-C+LSTM & - & 90.28 & 90.35 \\ \hline \hline \end{tabular}
\end{table}
Table 6: Ablation study results.  
Data Availability Statement: The data presented in this study are available on request from the corresponding author. The data are not publicly available due to privacy or ethical restrictions.  
Conflicts of Interest: [PERSON] and [PERSON] were employed by the Artificial Intelligence Department NARI Group Co., Ltd. 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.