A new approach of electrical appliance identification in residential buildings

Mohamed, Hamdi; Messaoud, Hassani; Bouguila, Nasreddine

doi:10.1016/j.epsr.2019.106037

Cited by 18 publications

(10 citation statements)

References 23 publications

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“…Once the machine is identified and its current energy consumption is known, action can be taken on the machine to achieve any or all DSM objectives. Even though scores of NILM techniques have been put forward by researchers, it was noted that the main problem is to have it as a reliable solution [8], [9]. An apparent problem with current NILM practice is related to the inaccuracy in the machine's identification and also to the application of complex techniques that eventually hinders users to take part in this technology.…”

Section: Introductionmentioning

confidence: 99%

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Development of site-specific non-intrusive load monitoring for maximum demand control

Mukhtaruddin

Hashim

Awang

et al. 2021

IJEECS

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Demand-side load management (DSM) requires greater role-play by end-users. To lower the investment for this load management concept, non-intrusive load management (NILM) was introduced as the solution. However, most of the mathematical techniques used in NILM are complex. This may hinder users from actively take part in the energy management effort. This paper explores the possibilities of applying change point detection techniques with help of differentiation and application of filters. These filters were selected strictly based on site-specific conditions. As part of the NILM implementation, a new and practical technique was developed for this paper. It was found that the developed technique, despite its simplicity it can identify the electrical equipment which added the significant load demand. The performance of the technique was found to be satisfactory as compared to results reported by other researchers.

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Section: Introductionmentioning

confidence: 99%

“…However, for this study, only differentiation and filtering were applied. A detailed explanation of the technique can be found in [8], [11]. While extensive review can be found in [17].…”

mentioning

confidence: 99%

Development of site-specific non-intrusive load monitoring for maximum demand control

Mukhtaruddin

Hashim

Awang

et al. 2021

IJEECS

View full text Add to dashboard Cite

show abstract

“…Researchers have extended the horizon of definitive cutting-edge techniques to innovate and authenticate robust NILM frameworks. In this regard, an optimal NILM algorithm based on event detection, feature extraction and shape matching techniques is proposed in [25]. Also, a demand side personalized recommendation system (PRS) is proposed in [26] that employs service recommendation techniques to infer residential users' potential interests and needs on energy saving appliances.…”

Section: Introductionmentioning

confidence: 99%

Comprehensive NILM Framework: Device Type Classification and Device Activity Status Monitoring Using Capsule Network

et al. 2020

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Non-intrusive load monitoring (NILM) discerns the individual electrical appliances of a residential or commercial building by disaggregating the accumulated energy consumption data without accessing to the individual components applying a single-point sensor. The fundamental concept is to decompose the aggregate load into a family of appliances that can explain its characteristics. In the age of smart grid networks and sophisticated energy management infrastructures, NILM can be considered as a significant tool pertaining to smart and inexpensive energy metering technique. In this article, a novel NILM solution based on capsule network is proposed, where convolutional neural network (CNN) is employed to extract potential features from a set of non-overlapping energy measurement data segments and the capsule architecture is designed to predict class probabilities of the individual segments. Then, a decision making algorithm is proposed to compute the final classification based on the predicted class probabilities of the segments. The presented research design comprises two unique NILM applications-device type classification from individual sensor recordings stored in COOLL and PLAID public databases, and device activity status monitoring at any particular time instant from aggregated energy consumption data recorded in UK-DALE database. Additionally, substantial experimental investigations have been carried out for device type classification accounting on various types of train and test set distributions as well as individual instrument and house classifications. The presented framework analyzes different parameters and metrics in depth to corroborate the efficacious performance evaluations for real-time applications. Relevant performance comparisons with existing works in literature validate the sustainability of the proposed solution.

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“…In this paper, the non-intrusive load identification process is studied. There are already various non-intrusive load identification methods [9][10][11]; however, most research works have been carried out based on a hypothetical condition that each electric appliance only has one running state. In fact, with the development of science and technology, not only the type of electric appliance but also the running state of each electric appliance is increased; that is, lots of electric appliances have multiple states.…”

Section: Introductionmentioning

confidence: 99%

Multi-State Household Appliance Identification Based on Convolutional Neural Networks and Clustering

et al. 2020

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Non-intrusive load monitoring, a convenient way to discern the energy consumption of a house, has been studied extensively. However, most research works have been carried out based on a hypothetical condition that each electric appliance has only one running state. This leads to low identification accuracy for multi-state electric appliances. To deal with this problem, a method for identifying the type and state of electric appliances based on a power time series is proposed in this paper. First, to identify the type of appliance, a convolutional neural network model was constructed that incorporated residual modules. Then, a k-means clustering algorithm was applied to calculate the number of states of the appliance. Finally, in order to identify the states of the appliances, different k-means clustering models were established for different multi-state electric appliances. Experimental results show effectiveness of the proposed method in identifying both the type and the running state of electric appliances.

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A new approach of electrical appliance identification in residential buildings

Cited by 18 publications

References 23 publications

Development of site-specific non-intrusive load monitoring for maximum demand control

Development of site-specific non-intrusive load monitoring for maximum demand control

Comprehensive NILM Framework: Device Type Classification and Device Activity Status Monitoring Using Capsule Network

Multi-State Household Appliance Identification Based on Convolutional Neural Networks and Clustering

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