Simultaneous Detection of Multiple Appliances From Smart-Meter Measurements via Multi-Label Consistent Deep Dictionary Learning and Deep Transform Learning

Singhal, Vanika; Maggu, Jyoti; Majumdar, Angshul

doi:10.1109/tsg.2018.2815763

Cited by 87 publications

(49 citation statements)

References 27 publications

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“…coffee machine in the morning but rarely in mid-night) [13]. Authors of [15] applied deep learning to the NILM while authors of [16] use a semi-supervised learning method to deal with situations of label missing in some training data.…”

Section: A Nilmmentioning

confidence: 99%

“…When more than one appliance is being monitored, the specific state of the appliance can be obtained by direct observation without using sensors to measure. The multi-label consistent deep transform learning (MLCDTL) and the multilabel consistent deep dictionary learning (MLCDDL) are applied to learn the multi-label classification for NILM problems by combining transform learning, dictionary learning, and deep learning [15]. The MLKNN is a multi-label classification variant being derived from the general k-nearest neighborhood classifier [17].…”

Section: B Multi-label Classification For Nilmmentioning

confidence: 99%

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New Appliance Detection for Nonintrusive Load Monitoring

Zhang

Chen

et al. 2019

IEEE Trans. Ind. Inf.

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Section: A Nilmmentioning

confidence: 99%

Section: B Multi-label Classification For Nilmmentioning

confidence: 99%

New Appliance Detection for Nonintrusive Load Monitoring

Zhang

Chen

et al. 2019

IEEE Trans. Ind. Inf.

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“…Supervised applications such as artificial neural networks, support vector machine applications, deep learning, feature learning, etc., use the training dataset of each appliance to identify and extract the features and build a feature dictionary [22][23][24][25][26][27][28]. In [23], a deep long short-term memory (LSTM) recurrent network is used to classify the types of electrical appliances into a set.…”

Section: Introductionmentioning

confidence: 99%

“…A deep convolutional neural network is used in [26] to implement a practical data reinforcement technique with the need of sub-metering for new unseen houses, which makes a post-processing technique to solve the NILM problem. In [27], load disaggregation based on deep learning methods is proposed, in which deep dictionary learning and deep transform learning techniques are used. The transform learning method is also proposed in [28] for solving the NILM problem.…”

Section: Introductionmentioning

confidence: 99%

Improving Residential Load Disaggregation for Sustainable Development of Energy via Principal Component Analysis

et al. 2020

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The useful planning and operation of the energy system requires a sustainability assessment of the system, in which the load model adopted is the most important factor in sustainability assessment. Having information about energy consumption patterns of the appliances allows consumers to manage their energy consumption efficiently. Non-intrusive load monitoring (NILM) is an effective tool to recognize power consumption patterns from the measured data in meters. In this paper, an unsupervised approach based on dimensionality reduction is applied to identify power consumption patterns of home electrical appliances. This approach can be utilized to classify household activities of daily life using data measured from home electrical smart meters. In the proposed method, the power consumption curves of the electrical appliances, as high-dimensional data, are mapped to a low-dimensional space by preserving the highest data variance via principal component analysis (PCA). In this paper, the reference energy disaggregation dataset (REDD) has been used to verify the proposed method. REDD is related to real-world measurements recorded at low-frequency. The presented results reveal the accuracy and efficiency of the proposed method in comparison to conventional procedures of NILM.

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“…In recent studies [4][5][6], NILM has been framed as a multilabel classification problem. These techniques make use of annotated aggregated load for training the model.…”

Section: Introductionmentioning

confidence: 99%

Multi Label Restricted Boltzmann Machine for Non-intrusive Load Monitoring

Verma

Singh

Majumdar

2019

ICASSP 2019 - 2019 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)

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Increasing population indicates that energy demands need to be managed in the residential sector. Prior studies have reflected that the customers tend to reduce a significant amount of energy consumption if they are provided with appliancelevel feedback. This observation has increased the relevance of load monitoring in today's tech-savvy world. Most of the previously proposed solutions claim to perform load monitoring without intrusion, but they are not completely nonintrusive. These methods require historical appliance-level data for training the model for each of the devices. This data is gathered by putting a sensor on each of the appliances present in the home which causes intrusion in the building. Some recent studies have proposed that if we frame Non-Intrusive Load Monitoring (NILM) as a multi-label classification problem, the need for appliance-level data can be avoided. In this paper, we propose Multi-label Restricted Boltzmann Machine(ML-RBM) for NILM and report an experimental evaluation of proposed and state-of-the-art techniques.

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Simultaneous Detection of Multiple Appliances From Smart-Meter Measurements via Multi-Label Consistent Deep Dictionary Learning and Deep Transform Learning

Cited by 87 publications

References 27 publications

New Appliance Detection for Nonintrusive Load Monitoring

New Appliance Detection for Nonintrusive Load Monitoring

Improving Residential Load Disaggregation for Sustainable Development of Energy via Principal Component Analysis

Multi Label Restricted Boltzmann Machine for Non-intrusive Load Monitoring

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