PMID- 34450806
OWN - NLM
STAT- MEDLINE
DCOM- 20210831
LR  - 20240403
IS  - 1424-8220 (Electronic)
IS  - 1424-8220 (Linking)
VI  - 21
IP  - 16
DP  - 2021 Aug 9
TI  - Non-Intrusive Load Monitoring for Residential Appliances with Ultra-Sparse Sample 
      and Real-Time Computation.
LID - 10.3390/s21165366 [doi]
LID - 5366
AB  - To achieve the goal of carbon neutrality, the demand for energy saving by the 
      residential sector has witnessed a soaring increase. As a promising paradigm to 
      monitor and manage residential loads, the existing studies on non-intrusive load 
      monitoring (NILM) either lack the scalability of real-world cases or pay 
      unaffordable attention to identification accuracy. This paper proposes a high 
      accuracy, ultra-sparse sample, and real-time computation based NILM method for 
      residential appliances. The method includes three steps: event detection, feature 
      extraction and load identification. A wavelet decomposition based standard 
      deviation multiple (WDSDM) is first proposed to empower event detection of 
      appliances with complex starting processes. The results indicate a false 
      detection rate of only one out of sixteen samples and a time consumption of only 
      0.77 s. In addition, an essential feature for NILM is introduced, namely the 
      overshoot multiple (which facilitates an average identification improvement from 
      82.1% to 100% for similar appliances). Moreover, the combination of modified 
      weighted K-nearest neighbors (KNN) and overshoot multiples achieves 100% 
      appliance identification accuracy under a sampling frequency of 6.25 kHz with 
      only one training sample. The proposed method sheds light on highly efficient, 
      user friendly, scalable, and real-world implementable energy management systems 
      in the expectable future.
FAU - Hu, Minzheng
AU  - Hu M
AD  - Department of Light Sources and Illuminating Engineering, Fudan University, 
      Shanghai 200433, China.
AD  - Shanghai Engineering Research Center for Artificial Intelligence and Integrated 
      Energy System, Fudan University, Shanghai 200433, China.
FAU - Tao, Shengyu
AU  - Tao S
AUID- ORCID: 0000-0001-5249-432X
AD  - Department of Light Sources and Illuminating Engineering, Fudan University, 
      Shanghai 200433, China.
AD  - Shanghai Engineering Research Center for Artificial Intelligence and Integrated 
      Energy System, Fudan University, Shanghai 200433, China.
AD  - Institute for Six-Sector Economy, Fudan University, Shanghai 200433, China.
FAU - Fan, Hongtao
AU  - Fan H
AD  - Department of Light Sources and Illuminating Engineering, Fudan University, 
      Shanghai 200433, China.
AD  - Shanghai Engineering Research Center for Artificial Intelligence and Integrated 
      Energy System, Fudan University, Shanghai 200433, China.
AD  - Institute for Six-Sector Economy, Fudan University, Shanghai 200433, China.
FAU - Li, Xinran
AU  - Li X
AD  - Department of Light Sources and Illuminating Engineering, Fudan University, 
      Shanghai 200433, China.
AD  - Shanghai Engineering Research Center for Artificial Intelligence and Integrated 
      Energy System, Fudan University, Shanghai 200433, China.
FAU - Sun, Yaojie
AU  - Sun Y
AD  - Department of Light Sources and Illuminating Engineering, Fudan University, 
      Shanghai 200433, China.
AD  - Shanghai Engineering Research Center for Artificial Intelligence and Integrated 
      Energy System, Fudan University, Shanghai 200433, China.
AD  - Institute for Six-Sector Economy, Fudan University, Shanghai 200433, China.
FAU - Sun, Jie
AU  - Sun J
AD  - Department of Light Sources and Illuminating Engineering, Fudan University, 
      Shanghai 200433, China.
AD  - Shanghai Engineering Research Center for Artificial Intelligence and Integrated 
      Energy System, Fudan University, Shanghai 200433, China.
LA  - eng
GR  - 18DZ1203305/the Key Scientific Research Program of Shanghai/
GR  - 2019YFB2103200/National Key Research and Development Program of China/
GR  - 2018YFB1500904/National Key Research and Development Program of China/
GR  - 202001015/Shanghai Municipal Economic and Information Commission/
GR  - 19DZ2252000/Shanghai Engineering Research Center for Artificial Intelligence 301 
      and Integrated Energy System/
PT  - Journal Article
DEP - 20210809
PL  - Switzerland
TA  - Sensors (Basel)
JT  - Sensors (Basel, Switzerland)
JID - 101204366
SB  - IM
MH  - *Algorithms
MH  - Cluster Analysis
PMC - PMC8400964
OTO - NOTNLM
OT  - non-intrusive load monitoring (NILM)
OT  - overshoot multiple
OT  - real-time computation
OT  - sparse sample
OT  - wavelet
OT  - weighted K-nearest neighbor (KNN)
COIS- The authors declare no conflict of interest.
EDAT- 2021/08/29 06:00
MHDA- 2021/09/01 06:00
PMCR- 2021/08/09
CRDT- 2021/08/28 01:01
PHST- 2021/07/02 00:00 [received]
PHST- 2021/07/25 00:00 [revised]
PHST- 2021/08/02 00:00 [accepted]
PHST- 2021/08/28 01:01 [entrez]
PHST- 2021/08/29 06:00 [pubmed]
PHST- 2021/09/01 06:00 [medline]
PHST- 2021/08/09 00:00 [pmc-release]
AID - s21165366 [pii]
AID - sensors-21-05366 [pii]
AID - 10.3390/s21165366 [doi]
PST - epublish
SO  - Sensors (Basel). 2021 Aug 9;21(16):5366. doi: 10.3390/s21165366.