Unsupervised NILM Implementation Using Odd Harmonic Currents

Papageorgiou, Petros G.; Gkaidatzis, Paschalis A.; Christoforidis, Georgios C.; Bouhouras, Aggelos S.

doi:10.1109/upec50034.2021.9548250

Cited by 8 publications

(5 citation statements)

References 14 publications

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“…In several recent studies, authors have proposed solutions based on the analysis of current harmonics [31][32][33][34]. Although their results are comparable to the objective of this work, there are some key differences, which are highlighted in Table I.…”

Section: Introductionmentioning

confidence: 58%

“…Compared to existing work, our proposed solutions were tested and verified for more low-powerconsuming appliances in a real, uncontrolled household environment. Further, several studies required data on appliance combinations to be fed into the database [10,15,[31][32][33][34] beforehand. However, the proposed solution only required data on individual appliances, yet accurately identifies appliances even when in sequential operations.…”

Section: Resultsmentioning

confidence: 99%

“…Moreover, the use of large-sized databases was a common attribute in most prior studies, as their solutions relied on data from individual appliances and all possible combinations [10,13,30,31]. When the number of appliances increases, these databases grow exponentially, leading to more memory use.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Real-Time Non-Intrusive Load Monitoring for Low-Power Appliances Using Odd Current Harmonics

Madhushan,

Perera,

Wijewardhana

et al. 2023

IJEETC

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Non-intrusive load monitoring (NILM) systems have gained immense popularity in the last two decades. Although many solutions were developed using the NILM approach, the majority of solutions failed to identify low-power-consuming appliances. However, a NILM system deployed in a domestic installation should be capable of identifying low-power-consuming appliances precisely. Therefore, we propose two real-time NILM solutions that can identify low-power residential appliances. These two solutions rely on active power and harmonic information extracted from the measured current waveform. We analyzed the performances of the two solutions using simulations and real-world experiments. Results indicate that both solutions can correctly identify appliances with an average accuracy above 90 percent.

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Section: Introductionmentioning

confidence: 58%

Section: Resultsmentioning

confidence: 99%

See 1 more Smart Citation

Real-Time Non-Intrusive Load Monitoring for Low-Power Appliances Using Odd Current Harmonics

Madhushan,

Perera,

Wijewardhana

et al. 2023

IJEETC

View full text Add to dashboard Cite

show abstract

“…They made judgments through a metric set consisting of matching degree [11], similarity degree [12], Hellinger distance [13], etc. The performance of LRA was also benefited from the development of machine learning, resulting in recognition methods with K-means clustering [14] and fuzzy C-means [15]. However, these methods basically utilized single feature without consideration on subtle differences between similar signals.…”

Section: Related Workmentioning

confidence: 99%

Learning-Based Non-Intrusive Electric Load Monitoring for Smart Energy Management

He,

Liu,

Zhang

et al. 2024

Preprint

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The state-of-the-art smart city has been calling for an economic but efficient energy management over large-scale network, especially for the electric power system. It is a critical issue to monitor, analyze and control electric loads of all users in system. In this paper, we employ the popular computer vision techniques of AI to design a non-intrusive load monitoring method for smart electric energy management. First of all, we utilize both signal transforms (including wavelet transform and discrete Fourier transform) and Gramian Angular Field (GAF) methods to map one-dimensional current signals onto two-dimensional color feature images. Second, we propose to recognize all electric loads from color feature images using a deep neural network with multi-scale feature extraction and attention mechanism. Third, we design our method as a cloud-based, non-intrusive monitoring of all users, thereby saving energy cost during electric power system control. Experimental results on both public and our private datasets have demonstrated our method achieves superior performances than its peers, and thus supports efficient energy management over large-scale Internet of Things.

show abstract

“…They made judgments through a metric set consisting of a matching degree [11], similarity degree [12], the Hellinger distance [13], etc. The performance of a LRA also benefited from the development of machine learning, resulting in recognition methods with K-means clustering [14] and fuzzy C-means clustering [15]. However, these methods basically utilized single features without considering subtle differences between similar signals.…”

Section: Introductionmentioning

confidence: 99%

Learning-Based Non-Intrusive Electric Load Monitoring for Smart Energy Management

He,

Liu,

Zhang

et al. 2024

Sensors

View full text Add to dashboard Cite

State-of-the-art smart cities have been calling for economic but efficient energy management over a large-scale network, especially for the electric power system. It is a critical issue to monitor, analyze, and control electric loads of all users in the system. In this study, a non-intrusive load monitoring method was designed for smart power management using computer vision techniques popular in artificial intelligence. First of all, one-dimensional current signals are mapped onto two-dimensional color feature images using signal transforms (including the wavelet transform and discrete Fourier transform) and Gramian Angular Field (GAF) methods. Second, a deep neural network with multi-scale feature extraction and attention mechanism is proposed to recognize all electrical loads from the color feature images. Third, a cloud-based approach was designed for the non-intrusive monitoring of all users, thereby saving energy costs during power system control. Experimental results on both public and private datasets demonstrate that the method achieves superior performances compared to its peers, and thus supports efficient energy management over a large-scale Internet of Things network.

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Unsupervised NILM Implementation Using Odd Harmonic Currents

Cited by 8 publications

References 14 publications

Real-Time Non-Intrusive Load Monitoring for Low-Power Appliances Using Odd Current Harmonics

Real-Time Non-Intrusive Load Monitoring for Low-Power Appliances Using Odd Current Harmonics

Learning-Based Non-Intrusive Electric Load Monitoring for Smart Energy Management

Learning-Based Non-Intrusive Electric Load Monitoring for Smart Energy Management

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