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Non-Intrusive Load Monitoring: A Review
被引:79
|作者:
Schirmer, Pascal A.
[1
,2
]
Mporas, Iosif
[1
]
机构:
[1] Univ Hertfordshire, Sch Engn & Comp Sci, Hatfield AL10 9AB, England
[2] BMW AG, Dept Power Elect, D-80809 Munich, Germany
关键词:
Energy consumption;
Load monitoring;
Task analysis;
Hidden Markov models;
Taxonomy;
Feature extraction;
Smart grids;
Energy disaggregation;
non-intrusive load monitoring (NILM);
smart meter;
smart grid;
ENERGY MANAGEMENT-SYSTEMS;
IDENTIFICATION ALGORITHM;
SOURCE SEPARATION;
NEURAL-NETWORK;
DISAGGREGATION;
NILM;
SIGNATURES;
TRANSFORM;
STATE;
D O I:
10.1109/TSG.2022.3189598
中图分类号:
TM [电工技术];
TN [电子技术、通信技术];
学科分类号:
0808 ;
0809 ;
摘要:
The rapid development of technology in the electrical energy sector within the last 20 years has led to growing electric power needs through the increased number of electrical appliances and automation of tasks. In parallel the global climate protection goals, energy conservation and efficient energy management arise interest for reduction of the overall energy consumption. These requirements have led to the recent adoption of smart-meters and smart-grids, as well as to the rise of Load Monitoring (LM) using energy disaggregation, also referred to as Non-Intrusive Load Monitoring (NILM), which enables appliance-specific energy monitoring by only observing the aggregated energy consumption of a household. The real-time information on appliance level can be used to get deeper insights in the origin of energy consumption and to make optimization, strategic load scheduling and demand management feasible. The three main contributions are as follows: First, a generalized up-to-date review of NILM approaches including a high-level taxonomy of NILM methodologies is provided. Second, previously published results are grouped based on the experimental setup which allows direct comparison. Third, the article is accompanied by a software implementation of the described NILM approaches.
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页码:769 / 784
页数:16
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