Investigation of electrical characteristics of residential light bulbs in load modelling studies with novel similarity score method

Doğan, Rasim

doi:10.1049/iet-gtd.2020.0674

Cited by 7 publications

(4 citation statements)

References 26 publications

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“…Compared with low-frequency data, high-frequency data can provide more load features. [12] and [13] were based on the improvement of the harmonic method. [12] carried out detailed harmonic analysis for various light bulbs and used total harmonic distortion rate, power, and current harmonics as features to calculate similarity scores to realize load identification.…”

Section: Related Work a Non-intrusive Load Monitoring (Nilm)mentioning

confidence: 99%

“…[12] and [13] were based on the improvement of the harmonic method. [12] carried out detailed harmonic analysis for various light bulbs and used total harmonic distortion rate, power, and current harmonics as features to calculate similarity scores to realize load identification. [13] proposed a method based on lower oddnumbered harmonics and bagging decision tree (FFT_BDT), which included two processes, getting the magnitude and phase at lower odd-numbered harmonics and using bagging decision tree to recognize loads.…”

Section: Related Work a Non-intrusive Load Monitoring (Nilm)mentioning

confidence: 99%

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Non-Intrusive Adaptive Load Identification Based on Siamese Network

Wang

et al. 2022

IEEE Access

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The traditional non-intrusive load monitoring (NILM) algorithms are mostly based on classification models, which have several deficiencies. Firstly, a large amount of labeled data is required to train the classification model. Secondly, these algorithms cannot identify unknown devices that frequently encountered in practical application. Finally, these models have poor performance in versatility which means they only adapt to the trained data. These shortcomings greatly influence the practicality of these NILM algorithms. To tackle these problems, this paper has proposed a non-intrusive adaptive load identification model based on the Siamese network, which uses both the V-I trajectory and active power as the load signatures. The Siamese network is utilized to calculate the similarity of the V-I trajectory, and the load identification is realized by matching the signature with the feature library. Through adding new features to the feature library dynamically, the identification of unknown load can be realized. In addition, the Siamese network is a typical network for few-shot learning, thus the proposed model can be trained with a small number of samples to achieve ideal recognition effect. At last, the validity and versatility of the model are verified in PLAID dataset and COOLL dataset.

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Section: Related Work a Non-intrusive Load Monitoring (Nilm)mentioning

confidence: 99%

Section: Related Work a Non-intrusive Load Monitoring (Nilm)mentioning

confidence: 99%

Non-Intrusive Adaptive Load Identification Based on Siamese Network

Wang

et al. 2022

IEEE Access

View full text Add to dashboard Cite

show abstract

“…High-frequency data has more load features than low-frequency data. The harmonic approach was improved in [11] and [12]. [11] employed total harmonic distortion rate, power, and current harmonics as characteristics to construct similarity scores to achieve load detection.…”

Section: Related Workmentioning

confidence: 99%

“…The harmonic approach was improved in [11] and [12]. [11] employed total harmonic distortion rate, power, and current harmonics as characteristics to construct similarity scores to achieve load detection. [12] proposed a method based on lower odd-numbered harmonics and a bagging decision tree (FFT BDT), which included two processes: obtaining magnitude and phase at lower odd-numbered harmonics and recognizing loads using a bagging decision tree.…”

Section: Related Workmentioning

confidence: 99%

Non-Intrusive Load Monitoring (NILM) Using a LSTM With Socio-Economic Parameters

Ahmed¹,

Taimoor²,

Javed³

et al. 2022

Preprint

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Nonintrusive load monitoring (NILM) deconstructs aggregated electrical usage data into individual appliances. The dissemination of disaggregated data to customers raises consumer awareness and encourages them to save power, lowering CO2 emissions to the environment. The performance of NILM systems has increased dramatically thanks to recent disaggregation methods. However, the capacity of these algorithms to generalize to various dwellings as well as the disaggregation of multi-state appliances remain significant obstacles. In this paper, we propose an energy disaggregation approach by using socio-economic parameters. The suggested approach helps in creating more accurate load profiles, which improves the accuracy and helps in better detection of the appliances. The proposed model outperforms state-of-the-art NILM techniques on the PRECON dataset. The mean absolute error reduces by 5% -10% on average across all appliances compared to the state-of-the-art. Thus, improving the detection of the target appliance in the aggregate measurement.

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A time-efficient factorial hidden Semi-Markov model for non-intrusive load monitoring

Wang

Zhang

et al. 2021

Electric Power Systems Research

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Investigation of electrical characteristics of residential light bulbs in load modelling studies with novel similarity score method

Cited by 7 publications

References 26 publications

Non-Intrusive Adaptive Load Identification Based on Siamese Network

Non-Intrusive Adaptive Load Identification Based on Siamese Network

Non-Intrusive Load Monitoring (NILM) Using a LSTM With Socio-Economic Parameters

A time-efficient factorial hidden Semi-Markov model for non-intrusive load monitoring

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