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. 2023 May 31;23(11):5226.
doi: 10.3390/s23115226.

A New NILM System Based on the SFRA Technique and Machine Learning

Simone Mari  1 , Giovanni Bucci  1 , Fabrizio Ciancetta  1 , Edoardo Fiorucci  1 , Andrea Fioravanti  1
Affiliations

A New NILM System Based on the SFRA Technique and Machine Learning

Simone Mari et al. Sensors (Basel). .

Abstract

In traditional nonintrusive load monitoring (NILM) systems, the measurement device is installed upstream of an electrical system to acquire the total aggregate absorbed power and derive the powers absorbed by the individual electrical loads. Knowing the energy consumption related to each load makes the user aware and capable of identifying malfunctioning or less-efficient loads in order to reduce consumption through appropriate corrective actions. To meet the feedback needs of modern home, energy, and assisted environment management systems, the nonintrusive monitoring of the power status (ON or OFF) of a load is often required, regardless of the information associated with its consumption. This parameter is not easy to obtain from common NILM systems. This article proposes an inexpensive and easy-to-install monitoring system capable of providing information on the status of the various loads powered by an electrical system. The proposed technique involves the processing of the traces obtained by a measurement system based on Sweep Frequency Response Analysis (SFRA) through a Support Vector Machine (SVM) algorithm. The overall accuracy of the system in its final configuration is between 94% and 99%, depending on the amount of data used for training. Numerous tests have been conducted on many loads with different characteristics. The positive results obtained are illustrated and commented on.

Keywords: machine learning (ML); nonintrusive load monitoring (NILM); smart home; support vector machine (SVM); sweep frequency response analysis (SFRA).

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Conflict of interest statement

The authors declare no conflict of interest.

Figures

Figure 1
Figure 1
SFRA applied to a star-connected electric machine.
Figure 2
Figure 2
SFRA system.
Figure 3
Figure 3
Installation of the SFRA in the test system.
Figure 4
Figure 4
Frequency response of individually powered household appliances.
Figure 5
Figure 5
Envelopes of the traces obtained in the presence and absence of the: (a) hairdryer, (b) microwave oven, (c) lamp, (d) laptop, (e) induction hob, (f) heater, (g) drill, and (h) TV.
Figure 5
Figure 5
Envelopes of the traces obtained in the presence and absence of the: (a) hairdryer, (b) microwave oven, (c) lamp, (d) laptop, (e) induction hob, (f) heater, (g) drill, and (h) TV.
Figure 6
Figure 6
Representation of a linear classification problem in which the samples are defined by only two features.
Figure 7
Figure 7
Representation of a non-linear classification problem in which the examples are defined by only two features.
Figure 8
Figure 8
The proposed structure.
Figure 9
Figure 9
The SFRA measurement system.
Figure 10
Figure 10
Coupling circuit for the signal generation section.
Figure 11
Figure 11
Coupling circuit for the signal acquisition section.
Figure 12
Figure 12
F1-Scores obtained for each considered sub-band.
Figure 13
Figure 13
Graphical comparison of the impact of the number of acquired points on the F1-Score.
Figure 14
Figure 14
Graphical comparison of the impact of the number of training samples on the F1-Score.
Figure 15
Figure 15
The frequency response of the input current to the EMI filter.
Figure 16
Figure 16
The scheme used for SPICE simulation.
See this image and copyright information in PMC

References

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