非侵入式负荷监测综述

邓晓平; 张桂青; 魏庆来; 彭伟; 李成栋

doi:10.16383/j.aas.c200270

非侵入式负荷监测综述

doi: 10.16383/j.aas.c200270

邓晓平^{1, 2,},
张桂青^{1, 2,},
魏庆来^3,,
彭伟^{1, 2,},
李成栋^{1, 2,}

1.
山东建筑大学信息与电气工程学院济南 250101
2.
山东省智能建筑技术重点实验室济南 250101
3.
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北京 100190

基金项目: 国家自然科学基金(61903226, 61573225), 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

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. 主要研究方向为智能控制方法, 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. 主要研究方向为智能控制, 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. 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. 主要研究方向为智能控制方法, 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

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出版历程
- 收稿日期: 2020-04-30
- 录用日期: 2020-08-27
- 网络出版日期: 2022-02-14
- 刊出日期: 2022-03-25

A Survey on the Non-intrusive Load Monitoring

DENG Xiao-Ping^{1, 2
,},
ZHANG Gui-Qing^{1, 2
,},
WEI Qing-Lai^3
,,
PENG Wei^{1, 2
,},
LI Cheng-Dong^{1, 2
,}

1.
School of Information and Electrical Engineering, Shandong Jianzhu University, Jinan 250101
2.
Shandong Provincial Key Laboratory of Intelligent Buildings Technology, Jinan 250101
3.
State Key Laboratory for Management and Control of Complex Systems, Institute of Automation, Chinese Academy of Sciences, Beijing 100190

Funds: Supported by National Natural Science Foundation of China (61903226, 61573225), 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)

More Information

Author Bio:
DENG Xiao-Ping　Lecturer at the School of Information and Electrical Engineering, Shandong Jianzhu University. He received his bachelor degree in electronic information science and technology and Ph.D. degree in communication and information systems from Wuhan University, in 2008 and 2013, respectively. His research interest covers communication signal processing and time series analysis

ZHANG Gui-Qing　Professor at the School of Information and Electrical Engineering, Shandong Jianzhu University. He received his bachelor degree from Shandong Jianzhu University, in 1986 and the Ph.D. degree from Xi＇an Jiaotong University, in 2002. His research interest covers intelligent control methods, intelligent buildings, smart home, and internet of things

WEI Qing-Lai　Professor at the State Key Laboratory for Management and Control of Complex Systems, Institute of Automation, Chinese Academy of Sciences. He received his Ph.D. degree in control theory and control engineering from Northeastern University China in 2009. His research interest covers intelligent control, artificial intelligence, learning systems, adaptive dynamic programming, adaptive optimal control, data-based control, neural network-based control, optimization in industrial systems, and smart grid

PENG Wei　Lecturer at the School of Information and Electrical Engineering, Shandong Jianzhu University. He received his master degree from Shandong Jianzhu University, in 2015 and Ph.D. degree from Shandong University, in 2018. His research interest covers intelligent control methods, internet of things, and energy efficiency management in smart buildings

LI Cheng-Dong　Professor at the School of Information and Electrical Engineering, Shandong Jianzhu University. He received his bachelor and master degrees from Shandong University, in 2004 and 2007, respectively, and Ph.D. degree from the Institute of Automation, Chinese Academy of Sciences, in 2010. His research interest covers artificial intelligence methods and applications, and smart building and smart city. Corresponding author of this paper

摘要

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.
- 非侵入式负荷监测 /
- 负荷分解 /
- 特征提取 /
- 隐马尔科夫模型 /
- 深度学习
Abstract: Non-intrusive load monitoring can realize the identification of individual electrical equipment and its working state by analyzing and processing the aggregated load data from electricity meters, which can be widely used in building energy conservation, smart cities, smart grids, etc. With the large-scale deployment of smart meters and the widespread application of various machine learning algorithms, non-intrusive load monitoring has aroused the common concern of academia and industry in recent years. This article reviews the research on non-intrusive load monitoring. First, the mathematical model and basic framework of non-intrusive load monitoring are refined, and then we separately summarize the data collection and pre-processing process, load disaggregation models and algorithms, data sets and evaluation metrics used in non-intrusive load monitoring. Finally, some challenges that the current research are confronted with are analyzed and we give some views on the future research.
- Non-intrusive load monitoring (NILM) /
- load disaggregation /
- feature extraction /
- hidden Markov model /
- deep learning

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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 journal article publications indexed by WoS (2010 ~ 2019)

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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

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图 3 非侵入式负荷监测结果示意图

Fig. 3 The illustration of NILM result

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图 4 非侵入式负荷监测典型流程图

Fig. 4 Typical flowchart of NILM

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图 5 负荷特征分类

Fig. 5 Taxonomy of load features

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图 6 负荷分解模型与算法分类

Fig. 6 Taxonomy of load disaggregation models and algorithms

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图 7 隐马尔科夫模型示意图

Fig. 7 The illustration of hidden Markov model

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图 8 因子隐马尔科夫模型示意图

Fig. 8 The illustration of factorial hidden Markov model

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图 9 具有4个节点的图示例

Fig. 9 A graph example with four nodes

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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

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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
HMM	模型直观	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

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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	20 s	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	1 s, 8 s (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); 6 s (Agg & Sub); 1 s (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); 1 s, 6 s (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	8 s	P	天然气表和环境数据
GREEND^[159]	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^[160]	美国	—	56	共 11 类, 大于 200 个设备	30 kHz	I, V
EMBED^[161]	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.

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