引用本文: | 保 富,黄祖源.基于流计算的大客户用能智能分析方法[J].电力系统保护与控制,2021,49(11):148-154.[点击复制] |
BAO Fu,HUANG Zuyuan.Intelligent analysis method for energy consumption of large customers based on stream computing[J].Power System Protection and Control,2021,49(11):148-154[点击复制] |
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摘要: |
针对大客户用电用能综合分析及无法及时发现用电异常的问题,设计了一种基于流计算下大客户用能智能分析方法。首先给出了大客户用能智能分析系统的整体构架,并从软硬件功能需求方面进行了描述。依托流计算技术实时处理能力和高性能数据吞吐能力实时处理采集到的数据,运用机器学习按照用户综合用能分析数学模型和窃电识别模型学习处理异常数据。并对高风险用户一定周期内的线损计算曲线、用能曲线以及电压、电流等电气数据进行展示,为大客户智能用能提供辅助支撑 |
关键词: 智能分析系统 大数据 大客户 窃电预警 流计算 |
DOI:DOI: 10.19783/j.cnki.pspc.200985 |
投稿时间:2020-08-13修订日期:2020-09-21 |
基金项目:南方电网科技项目资助(059300HK42180085) |
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Intelligent analysis method for energy consumption of large customers based on stream computing |
BAO Fu,HUANG Zuyuan |
(Information Center of Yunnan Power Grid Co., Ltd., Kunming 650217, China) |
Abstract: |
An intelligent analysis method of large customers energy consumption based on stream computing is designed to comprehensively analyze their power and energy consumption and deal with the inability of finding abnormal power consumption in time. First, the overall structure of the intelligent analysis system for the energy consumption of large customers is given, and the requirements for software and hardware functions are described. Relying on the real-time processing capabilities of stream computing technology and high-performance data throughput capabilities to process the collected data in real time, machine learning is used to learn to process abnormal data in accordance with the user's comprehensive energy consumption analysis mathematical model and electricity theft identification model. It also displays the line loss calculation curve, energy consumption curve, voltage, current and other electrical data within a certain period of high-risk users to provide auxiliary support for inspection work.
This work is supported by the Science and Technology Project of South China Power Grid (No. 059300HK42180085). |
Key words: intelligent analysis system big data large customers electricity theft warning stream computing |