针对用户不能及时、实时获得用电数据导致无法及时处理异常用电行为的问题,设计了一种基于物联网(Internet of Things,IoT)的实时异常用电行为的监测系统.该系统既可以采集单相电的电气参数也可以采集三相电的电气参数,以电能监测芯片ATT7022E和主控芯片STM32F103C8T6为核心的终端设备采集用电数据,无线传感模块窄带物联网(Narrow Band Internet of Things,NB-IoT)定时将采集的电流、电压和功率数据传送至云平台,云平台对数据流转和存储;使用eXtreme Gradient Boosting(XGBoost)算法对用电行为进行学习与预测,并在Web端进行数据管理、可视化和用电行为预测.经实验调试,该系统数据传输稳定、检测精度高.
Abstract
In order to solve the problem that users can't get real-time power consumption data,a real-time abnormal power consumption monitoring system based on the Internet of Things(IoT)is designed.The system can collect both single-phase and three-phase electrical parameters.Firstly,the terminal equipment with power monitoring chip ATT7022E and main control chip STM32F103C8T6 as the core collects power data.The wireless sensor module Narrow Band Internet of Things(NB-IoT)periodically transmits the collected current,voltage and power data to the cloud platform for data flow and storage.Secondly,eXtreme Gradient Boosting(XGBoost)algorithm is used to learn electricity consumption behavior and predict,and data management,visualization and electricity consumption behavior prediction are carried out on the Web side.Finally,the data transmission of the system is stable and the detection accuracy is high.
关键词
数据采集/用电行为/云平台/可视化
Key words
data acquisition/electricity consumption behavior/cloud platform/visualization
维普
万方数据