摘要
基于智能电表数据,采用数据驱动方法对居民用电模式进行识别,并提出电费节约策略。通过数据预处理、K-means聚类分析,识别出高峰、低谷和均衡3种典型用电模式。结合分时电价政策,构建电费优化模型,提出调整家电使用时间、应用智能控制设备、引导用户行为等策略。研究结果表明,这些策略可显著降低居民电费支出,同时促进电力系统高效运行和节能减排。未来,随着智能电表功能升级和大数据技术深化,该研究将为智能电网建设提供更有力支持。
Based on smart meter data,this article uses data-driven methods to identify residential electricity consumption patterns and proposes electricity cost saving strategies.Through data preprocessing and K-means clustering analysis,three typical electricity consumption patterns were identified:peak,valley,and equilibrium.Based on the time of use electricity pricing policy,construct an electricity cost optimization model and propose strategies such as adjusting the usage time of household appliances,applying intelligent control devices,and guiding user behavior.The research results indicate that these strategies can significantly reduce residents'electricity bills,while promoting efficient operation of the power system and energy conservation and emission reduction.In the future,with the upgrading of smart meter functions and the deepening of big data technology,this research will provide stronger support for the construction of smart grids.
作者
陆雨婷
岳德臣
王佳钰
LU Yuting;YUE Dechen;WANG Jiayu(State Grid Pizhou Power Supply Company,Xuzhou 221399,China)
出处
《电工技术》
2025年第S1期248-250,共3页
Electric Engineering
关键词
智能电表
数据驱动
用电模式识别
电费节约策略
smart meter
data driven
electricity usage pattern recognition
electricity cost saving strategy
