An Iterative Load Disaggregation Approach Based on Appliance Consumption Pattern

Wang, Huijuan; Yang, Wenrong

doi:10.3390/app8040542

Cited by 28 publications

(25 citation statements)

References 31 publications

(45 reference statements)

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“…Extremely high sampling rates enable to capture a richer set of harmonics as well as the electric noise [20,21]. Some authors have integrated the features derived from the consumption measurement with other information such as the frequency of use of household appliances [22,23] or weather conditions [24].…”

Section: Introductionmentioning

confidence: 99%

Non-Intrusive Load Disaggregation by Convolutional Neural Network and Multilabel Classification

2020

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Non-intrusive load monitoring (NILM) is the main method used to monitor the energy footprint of a residential building and disaggregate total electrical usage into appliance-related signals. The most common disaggregation algorithms are based on the Hidden Markov Model, while solutions based on deep neural networks have recently caught the attention of researchers. In this work we address the problem through the recognition of the state of activation of the appliances using a fully convolutional deep neural network, borrowing some techniques used in the semantic segmentation of images and multilabel classification. This approach has allowed obtaining high performances not only in the recognition of the activation state of the domestic appliances but also in the estimation of their consumptions, improving the state of the art for a reference dataset.

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Section: Introductionmentioning

confidence: 99%

Non-Intrusive Load Disaggregation by Convolutional Neural Network and Multilabel Classification

2020

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“…Variables such as time and duration of usage for a given event can be inferred just from the main power sensor [72]. In [73][74][75] the frequency of usage of an appliance, as well as the correlation of usage of multiple appliances, have been applied. This information can be extended with users' behaviour to express the uncertainty for each state of each appliance [76].…”

Section: Feature Setsmentioning

confidence: 99%

NILM Techniques for Intelligent Home Energy Management and Ambient Assisted Living: A Review

et al. 2019

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The ongoing deployment of smart meters and different commercial devices has made electricity disaggregation feasible in buildings and households, based on a single measure of the current and, sometimes, of the voltage. Energy disaggregation is intended to separate the total power consumption into specific appliance loads, which can be achieved by applying Non-Intrusive Load Monitoring (NILM) techniques with a minimum invasion of privacy. NILM techniques are becoming more and more widespread in recent years, as a consequence of the interest companies and consumers have in efficient energy consumption and management. This work presents a detailed review of NILM methods, focusing particularly on recent proposals and their applications, particularly in the areas of Home Energy Management Systems (HEMS) and Ambient Assisted Living (AAL), where the ability to determine the on/off status of certain devices can provide key information for making further decisions. As well as complementing previous reviews on the NILM field and providing a discussion of the applications of NILM in HEMS and AAL, this paper provides guidelines for future research in these topics.

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“…The evaluated datasets are tabulated in Table 1 with the number of appliances denoted in column '#App'. In the same column, the number of appliances in brackets is the number of appliances after excluding devices with power consumption below 25 W (italic entries), which were added to the power of the 'ghost device', similarly to the experimental set-up followed in [53,54]. The next three columns in Table 1 are tabulating the sampling period T s , the duration T and the appliance types of each evaluated dataset.…”

Section: Databasesmentioning

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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An Iterative Load Disaggregation Approach Based on Appliance Consumption Pattern

Cited by 28 publications

References 31 publications

Non-Intrusive Load Disaggregation by Convolutional Neural Network and Multilabel Classification

Non-Intrusive Load Disaggregation by Convolutional Neural Network and Multilabel Classification

NILM Techniques for Intelligent Home Energy Management and Ambient Assisted Living: A Review

Robust energy disaggregation using appliance-specific temporal contextual information

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