Transferability of Neural Network Approaches for Low-rate Energy Disaggregation

Murray, David M.; Stanković, Lina; Lulic, Srdjan; Sladojević, Srdjan

doi:10.1109/icassp.2019.8682486

Cited by 101 publications

(103 citation statements)

References 23 publications

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“…The integration of domain knowledge further enriches the design of CNN architectures. An on/off state classification subnetwork can be added in parallel to the regression sub-network so that the model can learn from on/off state information directly [2,18]. The work in this paper adopts the structure of subtask gated network (SGN) [2] as a starting point.…”

Section: Introductionmentioning

confidence: 99%

Scale- and Context-Aware Convolutional Non-Intrusive Load Monitoring

Chen

Zhang

Wang

et al. 2020

IEEE Trans. Power Syst.

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Non-intrusive load monitoring addresses the challenging task of decomposing the aggregate signal of a household's electricity consumption into appliance-level data without installing dedicated meters. By detecting load malfunction and recommending energy reduction programs, cost-effective nonintrusive load monitoring provides intelligent demand-side management for utilities and end users. In this paper, we boost the accuracy of energy disaggregation with a novel neural network structure named scale-and context-aware network, which exploits multi-scale features and contextual information. Specifically, we develop a multi-branch architecture with multiple receptive field sizes and branch-wise gates that connect the branches in the sub-networks. We build a self-attention module to facilitate the integration of global context, and we incorporate an adversarial loss and on-state augmentation to further improve the model's performance. Extensive simulation results tested on open datasets corroborate the merits of the proposed approach, which significantly outperforms state-of-the-art methods.

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Section: Introductionmentioning

confidence: 99%

Scale- and Context-Aware Convolutional Non-Intrusive Load Monitoring

Chen

Zhang

Wang

et al. 2020

IEEE Trans. Power Syst.

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“…Some NILM algorithms [15], [16] require priors to build a general model then tune it to a specific house. Some success with houses in the same country/region.…”

Section: A General Model Tuningmentioning

confidence: 99%

“…However, this has not been proven to be successful inter-country/region; e.i., having a model of a dishwasher in the UK will not work for disaggregating dishwasher in USA. This method is often referred to as transfer learning [16].…”

Section: A General Model Tuningmentioning

confidence: 99%

Universal Non-Intrusive Load Monitoring (UNILM) Using Filter Pipelines, Probabilistic Knapsack, and Labelled Partition Maps

Rodriguez-Silva

Makonin

2019

2019 IEEE PES Asia-Pacific Power and Energy Engineering Conference (APPEEC)

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Being able to track appliances energy usage without the need of sensors can help occupants reduce their energy consumption to help save the environment all while saving money. Non-intrusive load monitoring (NILM) tries to do just that. One of the hardest problems NILM faces is the ability to run unsupervised -discovering appliances without prior knowledge -and to run independent of the differences in appliance mixes and operational characteristics found in various countries and regions. We propose a solution that can do this with the use of an advanced filter pipeline to preprocess the data, a Gaussian appliance model with a probabilistic knapsack algorithm to disaggregate the aggregate smart meter signal, and partition maps to label which appliances were found and how much energy they use no matter the country/region. Experimental results show that relatively complex appliance signals can be tracked accounting for 93.7% of the total aggregate energy consumed.

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“…AMPd [23]). Approaches based on convolutional neural networks (CNNs) [24][25][26], recurrent neural networks (RNNs) [27,28] and long short-time memories (LSTMs) [27,29] have been proposed in the literature, while denoising autoencoders (dAEs) [30] and gate recurrent units (GRUs) [26] have also been used. Approaches with SS are based on single-channel source separation algorithms (e.g.…”

Section: Introductionmentioning

confidence: 99%

Robust energy disaggregation using appliance-specific temporal contextual information

Schirmer

Mporas

Sheikh-Akbari

2020

EURASIP J. Adv. Signal Process.

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An extension of the baseline non-intrusive load monitoring approach for energy disaggregation using temporal contextual information is presented in this paper. In detail, the proposed approach uses a two-stage disaggregation methodology with appliance-specific temporal contextual information in order to capture time-varying power consumption patterns in low-frequency datasets. The proposed methodology was evaluated using datasets of different sampling frequency, number and type of appliances. When employing appliance-specific temporal contextual information, an improvement of 1.5% up to 7.3% was observed. With the two-stage disaggregation architecture and using appliance-specific temporal contextual information, the overall energy disaggregation accuracy was further improved across all evaluated datasets with the maximum observed improvement, in terms of absolute increase of accuracy, being equal to 6.8%, thus resulting in a maximum total energy disaggregation accuracy improvement equal to 10.0%.

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Transferability of Neural Network Approaches for Low-rate Energy Disaggregation

Cited by 101 publications

References 23 publications

Scale- and Context-Aware Convolutional Non-Intrusive Load Monitoring

Scale- and Context-Aware Convolutional Non-Intrusive Load Monitoring

Universal Non-Intrusive Load Monitoring (UNILM) Using Filter Pipelines, Probabilistic Knapsack, and Labelled Partition Maps

Robust energy disaggregation using appliance-specific temporal contextual information

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