Robust event-based non-intrusive appliance recognition using multi-scale wavelet packet tree and ensemble bagging tree

Himeur, Yassine; Alsalemi, Abdullah; Bensaali, Fayçal; Amira, Abbes

doi:10.1016/j.apenergy.2020.114877

Cited by 97 publications

(39 citation statements)

References 60 publications

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“…To deeply investigate the performance of the proposed 2D‐PEP descriptor, its performance in terms of accuracy, F1 score, and time computation has been compared with other well‐known feature extraction schemes, usually deployed to extract pertinent characteristics of power signals. In this regard, various descriptors are considered in this comparative study, including root mean square (RMS), 44 S‐Transform, 45 mean absolute deviation (MAD), 46 multiscale wavelet packet tree (MSWPT), 13 slope sign change (SSC), 47 discrete wavelet transform (DWT), 48 and autoregressive (AR) 47 . Table 3 presents the obtained results, in which it can be seen that the 2D‐PEP outperforms the other descriptors in terms of accuracy and F1 score with respect to the data sets used in this framework.…”

Section: Resultsmentioning

confidence: 99%

“…It is responsible for discriminating between the various appliances after segregating aggregated power signals. 13,14 To do so with adequate accuracy, the NILM system should employ a robust feature extraction module followed by an appropriate powerful classifier. Extended efforts have been dedicated to design robust feature extraction descriptors and different NILM systems can be found in the literature based on the nature of feature extraction schemes they used.…”

Section: Introductionmentioning

confidence: 99%

“…Nonintrusive appliance recognition is among the core steps in designing a robust NILM system. It is responsible for discriminating between the various appliances after segregating aggregated power signals 13,14 . To do so with adequate accuracy, the NILM system should employ a robust feature extraction module followed by an appropriate powerful classifier.…”

Section: Introductionmentioning

confidence: 99%

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An intelligent nonintrusive load monitoring scheme based on 2D phase encoding of power signals

et al. 2020

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Nonintrusive load monitoring (NILM) is the de facto technique for extracting device-level power consumption fingerprints at (almost) no cost from only aggregated mains readings. Specifically, there is no need to install an individual meter for each appliance. However, a robust NILM system should incorporate a precise appliance identification module that can effectively discriminate between various devices. In this context, this paper proposes a powerful method to extract accurate power fingerprints for electrical appliance identification. Rather than relying solely on time-domain (TD) analysis, this framework abstracts the phase encoding of the TD description of power signals using a two-dimensional (2D) representation. This allows mapping power trajectories to a novel 2D binary representation space, and then performing a histogramming process after converting binary codes to new decimal representations. This yields the final histogram of 2D phase encoding of power signals, namely, 2D-PEP. An empirical performance evaluation conducted with three realistic power consumption databases collected at distinct resolutions indicates that the proposed 2D-PEP descriptor achieves

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Section: Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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An intelligent nonintrusive load monitoring scheme based on 2D phase encoding of power signals

et al. 2020

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“…Reducing the greenhouse emissions and decreasing its impact on climate change is a great challenge. Up to 85% of energy across the world is generated from nonrenewable resources such as oil, coal, and nuclear reactors 1 . While adopting renewable and green energy sources become a must, it is also of utmost importance to decrease the amount of wasted energy and develop strategies to optimize the use of this relevant resource 2 .…”

Section: Introductionmentioning

confidence: 99%

Smart power consumption abnormality detection in buildings using micromoments and improved K‐nearest neighbors

et al. 2021

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Anomaly detection in energy consumption is a crucial step towards developing efficient energy saving systems, diminishing overall energy expenditure and reducing carbon emissions. Therefore, implementing powerful techniques to identify anomalous consumption in buildings and providing this information to end‐users and managers is of significant importance. Accordingly, two novel schemes are proposed in this paper; the first one is an unsupervised abnormality detection based on one‐class support vector machine, namely UAD‐OCSVM, in which abnormalities are extracted without the need of annotated data; the second is a supervised abnormality detection based on micromoments (SAD‐M2), which is implemented in the following steps: (i) normal and abnormal power consumptions are defined and assigned; (ii) a rule‐based algorithm is introduced to extract the micromoments representing the intent‐rich moments, in which the end‐users make decisions to consume energy; and (iii) an improved K‐nearest neighbors model is introduced to automatically classify consumption footprints as normal or abnormal. Empirical evaluation conducted in this framework under three different data sets demonstrates that SAD‐M2 achieves both a highest abnormality detection performance and real‐time processing capability with considerably lower computational cost in comparison with other machine learning methods. For instance, up to 99.71% accuracy and 99.77% F1 score have been achieved using a real‐world data set collected at the Qatar University energy lab.

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“…In order to preserve what people deem to be a perfectly natural life; massive use of natural resources for buildingup energy, pollution, and global warming effects should be reduced and optimized by energy users [5,6]. For the sake of their environment and nature, they need for example to detach themselves from total reliance on fossil fuels or to cut-in demand to ensure fewer harmful carbon emissions in the atmosphere [7,8].…”

Section: Introductionmentioning

confidence: 99%

A Novel Approach for Detecting Anomalous Energy Consumption Based on Micro-Moments and Deep Neural Networks

et al. 2020

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Nowadays, analyzing, detecting, and visualizing abnormal power consumption behavior of householders are among the principal challenges in identifying ways to reduce power consumption. This paper introduces a new solution to detect energy consumption anomalies based on extracting micro-moment features using a rule-based model. The latter is used to draw out load characteristics using daily intent-driven moments of user consumption actions. Besides micro-moment features extraction, we also experiment with a deep neural network architecture for efficient abnormality detection and classification. In the following, a novel anomaly visualization technique is introduced that is based on a scatter representation of the micro-moment classes, and hence providing consumers an easy solution to understand their abnormal behavior. Moreover, in order to validate the proposed system, a new energy consumption dataset at appliance level is also designed through a measurement campaign carried out at Qatar University Energy Lab, namely, Qatar University dataset. Experimental results on simulated and real datasets collected at two regions, which have extremely different climate conditions, confirm that the proposed deep micro-moment architecture outperforms other machine learning algorithms and can effectively detect anomalous patterns. For example, 99.58% accuracy and 97.85% F1 score have been achieved under Qatar University dataset. These promising results establish the efficacy of the proposed deep micro-moment solution for detecting abnormal energy consumption, promoting energy efficiency behaviors, and reducing wasted energy.

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Robust event-based non-intrusive appliance recognition using multi-scale wavelet packet tree and ensemble bagging tree

Cited by 97 publications

References 60 publications

An intelligent nonintrusive load monitoring scheme based on 2D phase encoding of power signals

An intelligent nonintrusive load monitoring scheme based on 2D phase encoding of power signals

Smart power consumption abnormality detection in buildings using micromoments and improved K‐nearest neighbors

A Novel Approach for Detecting Anomalous Energy Consumption Based on Micro-Moments and Deep Neural Networks

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