###### Abstract
These models are complex deep learning constructs built upon the Transformer architecture [5]. With their parameters extending into the tens of billions, LLMs are celebrated as they have led to monumental strides in AI in recent times [6]. Notably, the GPT series developed by OpenAI exemplifies these innovations. The core advantage of LLMs is their proficiency in deciphering latent information from prompts and crafting pertinent responses. This ability is rooted in their extensive pre-training on diverse corpora and the foundational Transformer architecture. LLMs exhibit robust performance across a spectrum of NLP tasks by adapting to linguistic cues [7; 8]. Furthermore, LLMs can adeptly handle novel tasks with minimal adaptation, leveraging the Few-shot learning paradigm, thereby demonstrating their versatility and efficacy without the necessity for additional training. In the field of electricity, the prospective applications of LLMs are extensive. Their powerful representation capabilities and flexible transfer learning methods can be effectively applied to electricity consumption pattern analysis, power equipment fault diagnosis, and user feedback analysis. By integrating LLMs with other data processing and analysis technologies, the intelligence level and operational efficiency of power systems can be significantly enhanced. Specifically, LLMs can address the limitations of traditional AI models by providing more generalized and adaptive solutions, reducing the need for extensive feature engineering, and efficiently handling heterogeneous and massive datasets. While prior works have explored LLMs for time-series data, such as reprogramming LLMs for forecasting tasks [9] or leveraging pre-trained LLMs to capture complex dependencies in time series for applications like anomaly detection [10], these approaches often focus on predictive modeling rather than behavioral classification. For instance, studies in power systems have applied LLMs to load forecasting [11] and advanced dispatch optimization [12], but they typically overlook the integration of clustering for unlabeled pattern mapping with fine-tuned LLMs for downstream classification. Moreover, recent evaluations question the inherent utility of LLMs in preserving temporal structures for time-series tasks without domain-specific enhancements [13]. In contrast, our CLB-BER model introduces novelty by combining an ECDW-optimized CLUBS clustering algorithm--which dynamically aligns sequences while preserving temporal symmetry--with a fine-tuned DistilBERT for the robust classification of emerging electricity consumption patterns. This pipeline not only bridges unsupervised clustering with supervised LLM-based classification, but also outperforms traditional pipelines in handling multimodal heterogeneous power data, as demonstrated in our experiments. This paper explores the application of LLMs in classifying the electricity consumption patterns of users. We propose a novel method that integrates the improved CLUBS clustering algorithm with a fine-tuned BERT model, enhanced with a Softmax layer for downstream classification tasks, to improve the accuracy and efficiency of electricity consumption pattern classification. Comparative experiments demonstrate the effectiveness of this method in handling complex data and identifying new user consumption patterns. The primary contributions of this paper are as follows:  
1. We propose a novel LLM-based framework for the power sector with domain-specific fine-tuning to enhance distributed energy resource management, addressing the challenges of increasingly decentralized and complex energy systems. 2. We enhance the CLUBS clustering algorithm with the innovative ECDW method while maintaining Spark compatibility, enabling the more accurate classification of diverse electricity consumption patterns. 3. We develop CLB-BER, an advanced LLM-based classification model that employs our Improved-C algorithm, to label datasets and fine-tune a pre-trained DistilBERT model.