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. 2023 Aug 3;23(15):6926.
doi: 10.3390/s23156926.

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

Hussain Shareef  1 , Madathodika Asna  1 , Rachid Errouissi  1 , Achikkulath Prasanthi  1
Affiliations

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

Hussain Shareef et al. Sensors (Basel). .

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.

Keywords: current waveform; event detection; feature extraction; load identification; non-intrusive load monitoring; set theory.

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Conflict of interest statement

The authors declare no conflict of interest.

Figures

Figure 1
Figure 1
Architecture of proposed CRuST NILM.
Figure 2
Figure 2
Experimental setup for CRuST NILM.
Figure 3
Figure 3
Load current waveforms of selected loads.
Figure 4
Figure 4
Classification of load set in load library based on criteria array.
Figure 5
Figure 5
Waveforms corresponding to event detection using RMS and THD values of the aggregate load current iAgg.
Figure 6
Figure 6
Flowchart of event detection and search group definition.
Figure 7
Figure 7
Waveforms corresponding to preprocessing and framing stage to obtain load current iL.
Figure 8
Figure 8
Separation of iL into fundamental cycles as isig for feature extraction.
Figure 9
Figure 9
Flowchart of proposed load identification technique.
Figure 10
Figure 10
Confusion matrices of event detection algorithm for (a) scenario 1 (b) scenario 2, and (c) scenario 3.
Figure 11
Figure 11
Confusion matrices of load identification technique for (a) scenario 1, (b) scenario 2, and (c) scenario 3.
Figure 12
Figure 12
Confusion matrices for the proposed CRuST NILM for (a) scenario 1, (b) scenario 2, and (c) scenario 3.
Figure 13
Figure 13
ROC curve for the proposed CRuST NILM.
Figure 14
Figure 14
Confusion matrices for BPNM.
See this image and copyright information in PMC

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