Efficient Localness Transformer for Smart Sensor-Based Energy Disaggregation

Yue, Zhenrui; Zeng, Huimin; Kou, Ziyi; Shang, Lanyu; Wang, Dong

doi:10.1109/dcoss54816.2022.00035

Cited by 6 publications

(2 citation statements)

References 19 publications

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“…Kelly et al [17] CNN RNN UK-DALE Energy disaggregation appliance switch-on events Zhang et al [19] CNN REDD Energy disaggregation appliance switch-on Xia et al [18] RESNET REDD UK-DALE Energy disaggregation Song et al [29] LSTM REDD, UK-DALE Energy disaggregation appliance switch-on Rafiq et al [33] bi-LSTM UK-DALE ECO Energy disaggregation appliance switch-on Yue et al [35] BERT4NILM REDD UK-DALE Energy disaggregation appliance switch-on Yue et al [38] ELTRANSFORMER REDD UK-DALE Energy disaggregation appliance switch-on Sykiotis et al [39] ELECTRICITY REDD UK-DALE REFIT Energy disaggregation appliance switch-on for a 4-layer DB-LSTM, achieving a 66% improvement in F1 score.…”

Section: Methods Datasets Tasksmentioning

confidence: 99%

“…Nie et al [37] integrated ResNet with a transformer to capture the causal relationships and contextaware properties of the individual appliance power values in the overall power sequence. Yue et al [38] replaced selfattention with local attention to improve the poor performance in capturing local signal patterns. Sykiotis et al [39] proposed a purely transformer-based framework and introduced a novel training method.…”

Section: Methods Datasets Tasksmentioning

confidence: 99%

See 1 more Smart Citation

An intelligent non-intrusive load monitoring model based on power encoding and convolutional state modules

Xu,

Jiang,

Zhang

et al. 2024

Meas. Sci. Technol.

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Non-Intrusive Load Monitoring (NILM) identifies device power consumption or on/off states solely based on total power data, which is highly valuable for consumers to understand their appliance usage behavior and take necessary measures to reduce energy consumption, especially for the benefit of energy consumers' living production. However, a challenge faced by NILM is the tendency to focus excessively on power disaggregation while neglecting the disaggregation of on/off states, leading to lower classification accuracy, particularly owning to imbalanced states. This study proposes a model that integrates the power and on/off states to simultaneously disaggregate the power and device on/off states. The model comprises two main modules: a power encoding module for power disaggregation, and a convolutional state module for on/off state disaggregation. The power encoding module utilizes BERT-LSTM and Long Short-Term Memory (LSTM) networks for initial energy disaggregation. In contrast, the convolutional state module employs Convolutional Neural Networks (CNNs) for device state disaggregation. The output of the power-encoding module is multiplied by the probability of on/off states to obtain the final power.The proposed model is evaluated using the REDD and UK-DALE datasets. Compared to the baseline models, the results show an improvement in the device state classification average accuracy from 0.948 to 0.957, and a decrease in the average error between the real power and disaggregated power from 26.356W to 25.108W. Additionally, real-world experiments conducted using the designed platform for collecting and disaggregating power data achieve an average accuracy of 0.997. The proposed model demonstrates competitiveness in the NILM field and underscores its significance in aiding energy-consumption reduction efforts.

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