Current peak based device classification in NILM on a low-cost embedded platform using extra-trees

Jain, Aashish; Ahmed, Syed Shahbaaz; Sundaramoorthy, Prahalathan; Thiruvengadam, Raghavendran; Vijayaraghavan, Vineeth

doi:10.1109/urtc.2017.8284200

Cited by 11 publications

(4 citation statements)

References 9 publications

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“…The results obtained by processing the data available on the public BLUED dataset appeared very encouraging. The value obtained for the F1-score was 99.8%, which is higher than that obtained with other systems using the same dataset such as those proposed in [39] (91.5%) and [40] (93.2%).…”

Section: Conclusion and Final Remarkscontrasting

confidence: 65%

A New Convolutional Neural Network-Based System for NILM Applications

Ciancetta

Bucci

Fiorucci

et al. 2021

IEEE Trans. Instrum. Meas.

102

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Electrical load planning and demand response programs are often based on the analysis of individual load level measurements obtained from houses or buildings. The identification of individual appliances' power consumption is essential, since it allows improvements, which can reduce appliances' power consumption. In this paper, the problem of identifying the electrical loads connected to a house, starting from the total electric current measurement, is investigated. The proposed system is capable of extracting the energy demand of each individual device using a non-intrusive load monitoring (NILM) technique. A NILM algorithm based on a convolutional neural network is proposed. The proposed algorithm allows simultaneous detection and classification of events without having to perform double processing. As a result, the calculation times can be reduced. Another important advantage is that only the acquisition of current is required. The proposed measurement system is also described in this paper. Measurements are conducted using a test system, which is capable of generating the electrical loads found on a typical house. The most important experimental results are also included and discussed in the paper.

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Section: Conclusion and Final Remarkscontrasting

confidence: 65%

A New Convolutional Neural Network-Based System for NILM Applications

Ciancetta

Bucci

Fiorucci

et al. 2021

IEEE Trans. Instrum. Meas.

102

View full text Add to dashboard Cite

show abstract

“…The greatest difficulty encountered in the classification phase with the BLUED dataset is attributable to the significantly greater number of devices that the network is required to recognize, compared to those used for the acquired measurements. Furthermore, the value obtained for the F1-score is higher than that obtained with other systems using the same dataset, as those proposed in [36] (0.915) and [37] (0.932).…”

Section: Conclusion and Final Remarkscontrasting

confidence: 49%

Measurements for non-intrusive load monitoring through machine learning approaches

et al. 2021

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The topic of non-intrusive load monitoring (NILM) has seen a significant increase in research interest over the past decade, which has led to a significant increase in the performance of these systems. Nowadays, NILM systems are used in numerous applications, in particular by energy companies that provide users with an advanced management service of different consumption. These systems are mainly based on artificial intelligence algorithms that allow the disaggregation of energy by processing the absorbed power signal over more or less long time intervals (generally from fractions of an hour up to 24 h). Less attention was paid to the search for solutions that allow non-intrusive monitoring of the load in (almost) real time, that is, systems that make it possible to determine the variations in loads in extremely short times (seconds or fractions of a second). This paper proposes possible approaches for non-intrusive load monitoring systems operating in real time, analysing them from the point of view of measurement. The measurement and post-processing techniques used are illustrated and the results discussed. In addition, the work discusses the use of the results obtained to train machine learning algorithms that allow you to convert the measurement results into useful information for the user.

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“…The proposed system outperforms all three systems, as when all training data were provided (20 training samples for each scenario), the F1-Score achieved was 99.0%. It is important to note that the systems proposed in previous studies [62,63] were outperformed, even when the system was trained with the minimum number of samples when the system performance was 94.0%. The system is designed for local operation and is thus oriented toward edge implementation.…”

Section: Conclusion and Final Remarksmentioning

confidence: 90%

“…The proposed system demonstrated excellent performance, even when trained with a minimum number of samples. In order to provide a comparison against other pre-published literature in the field, works that used similar metrics [62][63][64] were considered. The performances achieved by the cited works, by evaluating the F1-Score, were 91.5%, 93.2%, and 98.0%, respectively.…”

Section: Conclusion and Final Remarksmentioning

confidence: 99%

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

Mari

Bucci

Ciancetta

et al. 2023

Sensors

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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.

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Current peak based device classification in NILM on a low-cost embedded platform using extra-trees

Cited by 11 publications

References 9 publications

A New Convolutional Neural Network-Based System for NILM Applications

A New Convolutional Neural Network-Based System for NILM Applications

Measurements for non-intrusive load monitoring through machine learning approaches

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

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