Open Access
Open access
Sensors, volume 23, issue 15, pages 6926

Rule-Based Non-Intrusive Load Monitoring Using Steady-State Current Waveform Features

Publication typeJournal Article
Publication date2023-08-03
Journal: Sensors
scimago Q1
wos Q2
SJR0.786
CiteScore7.3
Impact factor3.4
ISSN14243210, 14248220
Biochemistry
Analytical Chemistry
Atomic and Molecular Physics, and Optics
Electrical and Electronic Engineering
Instrumentation
Abstract

Monitoring electricity energy usage can help to reduce power consumption considerably. Among load monitoring techniques, non-intrusive load monitoring (NILM) provides a cost-efficient solution to identify individual load consumption details from the aggregate voltage and current measurements. Existing load monitoring techniques often require large datasets or use complex algorithms to obtain acceptable performance. In this paper, a NILM technique using six non-redundant current waveform features with rule-based set theory (CRuST) is proposed. The architecture consists of an event detection stage followed by preprocessing and framing of the current signal, feature extraction, and finally, the load identification stage. During the event detection stage, a change in connected loads is ascertained using current waveform features. Once an event is detected, the aggregate current is processed and framed to obtain the event-causing load current. From the obtained load current, the six features are extracted. Furthermore, the load identification stage determines the event-causing load, utilizing the features extracted and the appliance model. The results of the CRuST NILM are evaluated using performance metrics for different scenarios, and it is observed to provide more than 96% accuracy for all test cases. The CRuST NILM is also observed to have superior performance compared to the feed-forward back-propagation network model and a few other existing NILM techniques.

Found 

Top-30

Journals

1
1

Publishers

1
2
1
2
  • We do not take into account publications without a DOI.
  • Statistics recalculated only for publications connected to researchers, organizations and labs registered on the platform.
  • Statistics recalculated weekly.

Are you a researcher?

Create a profile to get free access to personal recommendations for colleagues and new articles.
Share
Cite this
GOST | RIS | BibTex | MLA
Found error?
Profiles