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非侵入式负荷监测综述

邓晓平 张桂青 魏庆来 彭伟 李成栋

邓晓平, 张桂青, 魏庆来, 彭伟, 李成栋. 非侵入式负荷监测综述. 自动化学报, 2020, 46(x): 1−20 doi: 10.16383/j.aas.c200270
引用本文: 邓晓平, 张桂青, 魏庆来, 彭伟, 李成栋. 非侵入式负荷监测综述. 自动化学报, 2020, 46(x): 1−20 doi: 10.16383/j.aas.c200270
Deng Xiao-Ping, Zhang Gui-Qing, Wei Qing-Lai, Peng Wei, Li Cheng-Dong. A survey on the non-intrusive load monitoring. Acta Automatica Sinica, 2020, 46(x): 1−20 doi: 10.16383/j.aas.c200270
Citation: Deng Xiao-Ping, Zhang Gui-Qing, Wei Qing-Lai, Peng Wei, Li Cheng-Dong. A survey on the non-intrusive load monitoring. Acta Automatica Sinica, 2020, 46(x): 1−20 doi: 10.16383/j.aas.c200270

非侵入式负荷监测综述

doi: 10.16383/j.aas.c200270
基金项目: 国家自然科学基金(61573225, 61903226), 山东省泰山学者计划(TSQN201812092), 山东省重点研发计划(2019GGX101072, 2019JZZY010115)), 山东省高等学校青创科技计划(2019KJN005)资助资助
详细信息
    作者简介:

    邓晓平:山东建筑大学信息与电气工程学院讲师. 2008年和2013年分别获武汉大学电子信息科学与技术学士学位和通信与信息系统博士学位. 主要研究方向为通信信号处理与时序信号分析. E-mail: dengxiaoping19@sdjzu.edu.cn

    张桂青:山东建筑大学信息与电气工程学院教授. 1986年获山东建筑大学学士学位, 2002年获西安交通大学博士学位. 主要研究方向包括智能控制方法、智能建筑、智能家居及物联网. E-mail: qqzhang@sdjzu.edu.cn

    魏庆来:中国科学院自动化研究所复杂系统管理与控制国家重点实验室副主任、研究员. 2009年获东北大学控制理论与控制工程专业博士学位. 主要从事智能控制、人工智能、自学习系统、自适应动态规划、自适应最优控制、数据驱动控制、神经网络控制、工业控制系统优化、智能电网等方面的研究工作. E-mail: qinglai.wei@ia.ac.cn

    彭伟:山东建筑大学信息与电气工程学院讲师. 2015年获山东建筑大学硕士学位, 2018年获山东大学博士学位. 主要研究方向包括智能控制方法、物联网及智能建筑能效管理. E-mail: pengwei19@sdjzu.edu.cn

    李成栋:山东建筑大学信息与电气工程学院教授. 2004年和2007年分别获山东大学学士和硕士学位, 2010获中科院自动化研究所博士学位. 主要研究方向为主要研究方向为人工智能方法及应用、智能建筑与智慧城市. 本文通信作者. E-mail: lichengdong@sdjzu.edu.cn

A Survey on the Non-intrusive Load Monitoring

Funds: Supported by National Natural Science Foundation of China (61573225, 61903226), Taishan Scholar Project of Shandong Province (TSQN201812092), Key Research and Development Program of Shandong Province (2019GGX101072, 2019JZZY010115) and the Youth Innovation Technology Project of Higher School in Shandong Province (2019KJN005)
  • 摘要: 非侵入式负荷监测通过对总负荷电表数据进行分析处理, 能够实现对各个用电设备及其工作状态的辨识, 可广泛应用于建筑节能、智慧城市、智能电网等领域. 近年来, 随着智能电表的大规模部署以及各类机器学习算法的广泛应用, 非侵入式负荷监测引起了学术界与工业界的共同关注. 本文对非侵入式负荷监测方面的研究进行综述. 首先提炼非侵入式负荷监测的问题模型及基本框架; 然后分别对非侵入式负荷监测的数据采集与预处理过程、负荷分解模型与方法、常用数据集及评估指标进行归纳总结; 最后, 对目前研究中存在的挑战进行分析, 并对未来的研究方向进行展望.
  • 图  1  WoS数据库中相关期刊论文数量分布(2010-2019)

    Fig.  1  Number distribution of related JA publications indexed by WoS(2010-2019)

    图  2  典型的非侵入式负荷监测系统框图

    Fig.  2  The diagram of a typical NILM system

    图  3  非侵入式负荷监测结果示意图

    Fig.  3  The illustration of NILM result

    图  4  非侵入式负荷监测典型流程图

    Fig.  4  Typical flowchart of NILM

    图  5  负荷特征分类

    Fig.  5  Taxonomy of load features

    图  6  负荷分解模型与算法分类

    Fig.  6  Taxonomy of load disaggregation models and algorithms

    图  7  隐马尔可夫模型示意图

    Fig.  7  The illustration of hidden markov model

    图  8  因子隐马尔可夫模型示意图

    Fig.  8  The illustration of factorial hidden markov model

    图  9  具有4个节点的图示例

    Fig.  9  A graph example with four nodes

    图  10  基于自动编码器的非侵入式负荷监测网络结构图

    Fig.  10  Network structure diagram of NILM based on automatic encoder

    表  1  NILM方法优缺点对比

    Table  1  Comparison of NILM methods

    方法 优点 缺点
    HMM 模型直观 运算量大
    精确推理困难
    GSP 表征能力强 现有算法相对较少
    训练周期短
    运算复杂度较低
    ML 自动提取特征 模型参数多
    分解准确率高 训练所需数据量大
    泛化性能好 可解释性差
    下载: 导出CSV

    表  2  非侵入式负荷监测公开数据集

    Table  2  Publicly available datasets for NILM

    数据集 地点 采集持续时间 房屋
    数量
    传感器数量/
    房屋
    采集频率 采集参数1 其他数据
    REDD[9] 美国 几天-数月 6 10-24 15 kHz(Agg); 0.5 Hz, 1 Hz(Sub) V、P(Agg); P(Sub)
    BERDS[149] 美国 1年 1 NA 20sec P, Q, S 气候数据
    BLUED[150] 美国 8天 1 1 12 kHz(Agg); 20 Hz(Sub) I、V (P通过计算得出@60 Hz) 各设备的状态转换标签
    Smart* Home[151] 美国 3个月 3 A:26; B, C:21 1 Hz P, S (Agg); P(Sub) 太阳能和风力发电数据, 气候,
    室内温湿度数据
    DRED[152] 荷兰 6个月 3 12 1 Hz E, P (Agg&Sub) 室内外温度, 风速, 降水, 入住率,
    房屋布局, 设备配置, 无线信号
    Tracebase[153] 德国 1883天
    (累计)
    15 NA 1sec, 8sec(Sub) P(Sub) 用于设备识别, 未采集总表数据
    AMPds2[21, 154] 加拿大 2年 1 21 1 min V, I, F, P, Q, S, F, E等10项 水表、天然气表数据,
    电费账单数据
    UK-DALE[155] 英国 2个月-4.3年 5 5-54 16 kHz(I, V of Agg); 6sec(Agg&Sub);1sec(Agg) P, I, V 设备状态切换信息,
    住户人员构成
    及能源构成信息
    iAWE[156] 印度 73天 1 33 1 Hz(Agg); 1sec, 6sec(Sub) V, I, F, P, ph 用水量和环境数据 (温度, 人员
    活动, 声音及无线信号强度)
    REFIT[157] 英国 2年 20 10 8sec P 天然气表和环境数据
    GREEND[158] 意大利/奥地利 1年 9 9 1 Hz P 用电负荷配置, 住户情况描述
    ECO[34] 瑞士 8个月 6 6-10 1 Hz P、Q 住户情况描述
    PLAID[162] 美国 - 56 共11类, 大于200个设备 30 kHz I、V
    EMBED[163] 美国 14-27天 3 共21类, 约40个设备 12 kHz(Agg) 1-2 Hz(Sub) I、V、P、Q、F 各设备的状态转换标签, 入住率
    1 Agg: 总表 Sub: 分表 P: 有功功率 Q: 无功功率 S: 视在功率 E: 电量 F: 频率 V: 电压 I: 电流 ph: 相位
    下载: 导出CSV
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