| 刘同阳,张强,胡新雨,徐晓轶,毛艳芳,吕晓祥,李正佳,孙大军.一种大语言模型支持的用电异常智能检测方法[J].电力需求侧管理,2026,28(1):120-125 |
| 一种大语言模型支持的用电异常智能检测方法 |
| An intelligent detection method for electrical anomalies with large language models |
| 投稿时间：2025-09-22 修订日期：2025-10-28 |
| DOI：10.3969/j.issn.1009-1831.2026.01.017 |
| 中文关键词: 用户用电量时间序列 用电异常检测 大语言模型 提示工程 |
| 英文关键词: consumer electricity consumption time series anomaly detection of power utility large language modeling prompt engineering |
| 基金项目:国家电网公司总部科技项目(5700-202318598A-3-2-ZN) |
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| 中文摘要: |
| 时间序列分析在工业领域极其重要。精准识别时间序列中的异常,能够有效提高生产效率,降低生产成本。针对电力使用,用户用电量时间序列的异常检测对于降低电力公司的运用成本、提升其服务质量具有重要的作用。随着大语言模型的快速发展,大语言模型被用在越来越多的领域,包括新兴的时间序列预测。因此提出一种基于大语言模型的用户用电时间序列异常检测方法,该方法通过提示工程与大语言模型交流,通过对时间序列进行预处理,使大语言模型能够理解输入数据信息,并正确执行异常检测任务。通过在用户用电时间序列数据集上进行实验分析,结果表明,与现有的异常检测模型相比,所提方法能够有效检测出异常数据。 |
| 英文摘要: |
| Time series analysis is considered extremely important in the industrial field, anomalies in time series can be accurately identified to effectively improve production efficiency and reduce costs, and for electricity usage, operating costs of power companies are reduced and service quality is improved through anomaly detection in users' electricity consumption time series. With the rapid development of large language models, they are being applied to more fields, including time series prediction. A method for anomaly detection of users' electricity consumption time series based on large language models is proposed, communication with large language models is achieved via prompt engineering, the time series is preprocessed so that input data can be understood by the models and the anomaly detection task can be correctly executed, and experimental analysis on real user electricity consumption data shows that abnormal data can be effectively detected by the proposed method compared with existing models. |
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