Estimation of Target Appliance Electricity Consumption Using Background Filtering

Cui, Gaochen; Liu, Bo; Luan, Wenpeng; Yu, Yixin

doi:10.1109/tsg.2019.2892841

Cited by 48 publications

(18 citation statements)

References 25 publications

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“…For the low-frequency data, the NILM methods can be divided into methods based on combinatorial optimization (CO) [3][4], methods based on matching [8][9] and methods based on classification [10][11]. In [3], the NILM problems were regarded as classical convex optimization problems, which can be solved by using mixed integer linear programming (MILP) solver CPLEX to enhance calculation efficiency.…”

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

confidence: 99%

“…The author also used pre-elimination to reduce appliance combinations and combined it with Appliance Usage Patterns (AUPs) to improve the identification accuracy. In [8], a background filtering algorithm was proposed to avoid the difficulty of obtaining labeled aggregated data. This algorithm can complete model training using only aggregate data and target device operating curves.…”

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

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show abstract

“…Finally, some authors employed specialized strategies: The authors of [35] found that by adding varying offsets specifically to the on state of the fridge, they were able to greatly enhance the corresponding disaggregation performance. So-called 'background filtering' has been proposed by [69] to remove all windows in the aggregate load curve that contain the target appliance. Activations from the target appliance are then added randomly to the filtered aggregate to create synthetic data for training.…”

Section: Data Augmentationmentioning

confidence: 99%

Review on Deep Neural Networks Applied to Low-Frequency NILM

et al. 2021

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This paper reviews non-intrusive load monitoring (NILM) approaches that employ deep neural networks to disaggregate appliances from low frequency data, i.e., data with sampling rates lower than the AC base frequency. The overall purpose of this review is, firstly, to gain an overview on the state of the research up to November 2020, and secondly, to identify worthwhile open research topics. Accordingly, we first review the many degrees of freedom of these approaches, what has already been done in the literature, and compile the main characteristics of the reviewed publications in an extensive overview table. The second part of the paper discusses selected aspects of the literature and corresponding research gaps. In particular, we do a performance comparison with respect to reported mean absolute error (MAE) and F1-scores and observe different recurring elements in the best performing approaches, namely data sampling intervals below 10 s, a large field of view, the usage of generative adversarial network (GAN) losses, multi-task learning, and post-processing. Subsequently, multiple input features, multi-task learning, and related research gaps are discussed, the need for comparative studies is highlighted, and finally, missing elements for a successful deployment of NILM approaches based on deep neural networks are pointed out. We conclude the review with an outlook on possible future scenarios.

show abstract

“…The authors of [76] found that by adding varying offsets specifically to the on state of the fridge, they were able to greatly enhance the corresponding disaggregation performance. So called 'background filtering' has been proposed by [86] to remove all windows in the aggregate load curve that contain the target appliance. Activations from the target appliance are then added randomly to the filtered aggregate to create synthetic data for training.…”

Section: Data Augmentationmentioning

confidence: 99%

Review on Deep Neural Networks Applied to Low-Frequency NILM

Huber¹,

Calatroni²,

Rumsch³

et al. 2021

Preprint

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This paper reviews non-intrusive load monitoring (NILM) approaches that employ deep neural networks to disaggregate appliances from low frequency data, i.e. data with sampling rates lower than the AC base frequency. We first review the many degrees of freedom of these approaches, what has already been done in literature, and compile the main characteristics of the reviewed publications in an extensive overview table. The second part of the paper discusses selected aspects of the literature and corresponding research gaps. In particular, we do a performance comparison with respect to reported MAE and F$_1$-scores and observe different recurring elements in the best performing approaches, namely data sampling intervals below 10\,s, a large field of view, the usage of GAN losses, multi-task learning, and post-processing. Subsequently, multiple input features, multi-task learning and related research gaps are discussed, the need for comparative studies is highlighted, and finally, missing elements for a successful deployment of NILM approaches based on deep neural networks are pointed out. We conclude the review with an outlook on possible future scenarios.

show abstract

Estimation of Target Appliance Electricity Consumption Using Background Filtering

Cited by 48 publications

References 25 publications

Non-Intrusive Adaptive Load Identification Based on Siamese Network

Non-Intrusive Adaptive Load Identification Based on Siamese Network

Review on Deep Neural Networks Applied to Low-Frequency NILM

Review on Deep Neural Networks Applied to Low-Frequency NILM

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