Development of a real-time non-intrusive appliance load monitoring system: An application level model

Nguyen, Trung Kiên; Dekneuvel, Eric; Jacquemod, G.; Nicolle, B.; Zammit, Olivier; Nguyễn, Văn Minh

doi:10.1016/j.ijepes.2017.01.012

Cited by 29 publications

(9 citation statements)

References 8 publications

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“…Moreover, appliances were often switched on/off when one or even zero appliances were operating in the background. As an example, in Reference [48], the washing machine identification was performed with high accuracy, but the figure presenting the testing sequence shows that only one appliance was operating in the background.…”

Section: Comparison Of Nonintrusive Appliance Load Monitoring Methodsmentioning

confidence: 99%

Nonintrusive Appliance Load Monitoring: An Overview, Laboratory Test Results and Research Directions

Wójcik

Łukaszewski

Kowalik

et al. 2019

Sensors

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Nonintrusive appliance load monitoring (NIALM) allows disaggregation of total electricity consumption into particular appliances in domestic or industrial environments. NIALM systems operation is based on processing of electrical signals acquired at one point of a monitored area. The main objective of this paper was to present the state-of-the-art in NIALM technologies for the smart home. This paper focuses on sensors and measurement methods. Different intelligent algorithms for processing signals have been presented. Identification accuracy for an actual set of appliances has been compared. This article depicts the architecture of a unique NIALM laboratory, presented in detail. Results of developed NIALM methods exploiting different measurement data are discussed and compared to known methods. New directions of NIALM research are proposed.

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Section: Comparison Of Nonintrusive Appliance Load Monitoring Methodsmentioning

confidence: 99%

Nonintrusive Appliance Load Monitoring: An Overview, Laboratory Test Results and Research Directions

Wójcik

Łukaszewski

Kowalik

et al. 2019

Sensors

View full text Add to dashboard Cite

show abstract

“…There are two loss functions called soft loss and hard loss shown given in the formula by Le et al (2015) and Nguyen et al (2017), respectively.…”

Section: Deit Modelmentioning

confidence: 99%

“…Some start-up transient feature parameters with clear physical meanings are designed for motor load identification (Liu et al, 2022). Also, many researchers apply different mathematical manipulations, such as fast Fourier transform (FFT) (Nguyen et al, 2017;Bouhouras et al, 2017), discrete wavelet transformation (DWT) (Chang H H et al, 2013), and Hilbert transform (HT) (Gabaldón et al, 2014), to deal with high-frequency time series and gain abundant information in the frequency domain, while a normalized steady-state voltage and current signals during one cycle called V-I trajectories are considered distinguishable load signatures (Hassan et al, 2013;Wang et al, 2018). In the study by Gao et al, (2015), contrast examples based on the PLAID public dataset certify that V-I trajectories have more advantages in identification than other high-frequency features.…”

Section: Introductionmentioning

confidence: 99%

A Non-Intrusive Residential Electric Bicycle Load Monitoring Method Based on Hybrid Feature Bitmap and DeiT

et al. 2022

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Taking into account energy management and fire safety, electric bicycles are one of the most significant household loads that require real-time sensing for nonintrusive load monitoring. V–I trajectories, power quantities, and harmonic characteristics are the basic selection in feature space for appliance identification. Based on the study of the charging mode of electric bicycles, this study expands the V–I trajectory into V-△I trajectory for gaining the load signature with multi appliances working simultaneously. We perform linear interpolation and pixelation to obtain a bitmap of the V-△I trajectory. Meanwhile, active and harmonic features are encoded and combined to form a hybrid feature bitmap, which is unique to compensate for the high harmonic feature loss caused by the pixelation of the V-△I trajectory. Furthermore, we trained the DeiT model on the self-built dataset and UK-DALE dataset and performed two experiments under single and superposition working conditions for electric bicycles. Our case results indicate that the DeiT model using hybrid feature bitmap offers better overall precision in the prediction of electric bicycles, against other deep convolutional neural networks.

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“…Load monitoring was first proposed by Hart [7]. The nonintrusive load monitoring method, which uses a finite state approach to describe the state transitions of electric appliances, was then proposed based on steady-state power variation research at power access points.…”

Section: Related Workmentioning

confidence: 99%

Non-intrusive load monitoring algorithm based on household electricity use habits

Yin

et al. 2021

Neural Comput & Applic

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The construction of smart grid is an important part of improving the utilization rate of electric energy. As an important way for the construction of smart grid, non-intrusive load decomposition methods have been extensively studied. In this type of method, limited by transmission cost and network bandwidth, low-frequency data has been widely used in practical applications. However, the accuracy of device identification in this case faces challenges. Due to the relatively single characteristics of low-frequency data, it is difficult to express the operating status of complex electrical appliances, resulting in low decomposition performance. In this paper, a non-intrusive load is proposed based on household electrical habits by studying the relationship between household electricity consumption habits and load status decomposition method. The Gaussian mixture model and time information are used to model the probability distribution of the electrical appliance state. This probability distribution is then used as the observation probability distribution of the factor hidden Markov model. In such a way, the BH-FHMM model is proposed. Finally, load decomposition is carried out through the load decomposition process of the FHMM model. In order to verify the performance of the proposed method, an experimental comparison is conducted based on the REDD data set. According to the results, a significant improvement in equipment recognition accuracy is obtained.

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Development of a real-time non-intrusive appliance load monitoring system: An application level model

Cited by 29 publications

References 8 publications

Nonintrusive Appliance Load Monitoring: An Overview, Laboratory Test Results and Research Directions

Nonintrusive Appliance Load Monitoring: An Overview, Laboratory Test Results and Research Directions

A Non-Intrusive Residential Electric Bicycle Load Monitoring Method Based on Hybrid Feature Bitmap and DeiT

Non-intrusive load monitoring algorithm based on household electricity use habits

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