Non-Intrusive Load Monitoring of Buildings Using Spectral Clustering

Ghaffar, Muzzamil; Sheikh, Shakil R.; Naseer, Noman; Din, Zia Mohy Ud; Rehman, Hafiz Zia Ur; Naved, Muhammad

doi:10.3390/s22114036

Cited by 11 publications

(12 citation statements)

References 53 publications

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“…In ILM [8,9], each electric load is monitored by a separate sensor and the information acquired from all sensors can be centrally processed by cloud-end. While in NILM [6,7,10], only one monitor is required for each family or cell. It captures electric signals (such as voltage, current, and so on) at the commercial power input and transimits them to cloud server in which workload information of all loads are interpreted with algorithms.…”

Section: Related Workmentioning

confidence: 99%

Learning-Based Non-Intrusive Electric Load Monitoring for Smart Energy Management

He,

Liu,

Zhang

et al. 2024

Preprint

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The state-of-the-art smart city has been calling for an economic but efficient energy management over large-scale network, especially for the electric power system. It is a critical issue to monitor, analyze and control electric loads of all users in system. In this paper, we employ the popular computer vision techniques of AI to design a non-intrusive load monitoring method for smart electric energy management. First of all, we utilize both signal transforms (including wavelet transform and discrete Fourier transform) and Gramian Angular Field (GAF) methods to map one-dimensional current signals onto two-dimensional color feature images. Second, we propose to recognize all electric loads from color feature images using a deep neural network with multi-scale feature extraction and attention mechanism. Third, we design our method as a cloud-based, non-intrusive monitoring of all users, thereby saving energy cost during electric power system control. Experimental results on both public and our private datasets have demonstrated our method achieves superior performances than its peers, and thus supports efficient energy management over large-scale Internet of Things.

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Section: Related Workmentioning

confidence: 99%

Learning-Based Non-Intrusive Electric Load Monitoring for Smart Energy Management

He,

Liu,

Zhang

et al. 2024

Preprint

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“…[6] UK-DALE [13], REDD [14], Refi [15] Small X [7] UK-DALE [13], REDD [14], Refi [15] Small X [8] Refi [15] Small X…”

Section: Dataset Usedmentioning

confidence: 99%

“…A similar approach is based on the analysis of the eigenvalue's spectrum of the Laplacian representing the network of appliances contributing to energy aggregation. It has been successfully proposed by Ghaffar et al In [8], it is shown that the robust eigenvalue evaluation algorithm allows the indirect determination of the weight of each load in the measured energy.…”

mentioning

confidence: 99%

Non-Intrusive Load Monitoring

Fortuna

Buscarino

2022

Sensors

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“…These machine learning techniques are divided in supervised and unsupervised techniques [10]. In a previous study [11], authors proposed two unsupervised techniques for NILM applications; Spectral Cluster-Mean (SC-M) and Spectral Cluster-Eigen Vector (SC-EV).…”

Section: Introductionmentioning

confidence: 99%

“…Spectral Cluster-Mean (SC-M) method clusters data on the basis of the mean value of device power signature [11]. A linear similarity graph is defined with consecutive nodes connected and others unconnected with weights defined according to the adjacency matrix;…”

Section: Introductionmentioning

confidence: 99%

Accuracy Improvement of Non-Intrusive Load Monitoring Using Voting-Based Consensus Clustering

et al. 2023

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Widespread inculcation of smart meter data in modern grid is motivating stakeholders to utilize it for demand response management and achieving energy sustainability goals. One of the methods being used in this regard is Non-Intrusive Load Monitoring (NILM); for disaggregating individual devices from a combined load profile. This study combines two spectral clustering strategies using voting-based consensus clustering technique in such a way as to achieve the benefits of both parent strategies. The voters in the consensus are taken to be the solutions proposed by Spectral Cluster-Mean (SC-M) and Spectral Cluster-Eigen Vector (SC-EV) algorithms with different window sizes to achieve diversity. Currently, Spectral Clustering for NILM has been used by few research works and so far, no one technique has achieved higher accuracy in detecting various kinds of devices. The proposed strategy was evaluated on real world data set (REFIT). The results have shown enhanced overall performance by up to 6%. An in-depth analysis of various tuning parameters of SC-M and SC-EV is also presented. These novel contributions increase the feasibility of using spectral clustering and voting based consensus clustering for NILM and may open further avenues of research in this direction.INDEX TERMS Spectral clustering, voting based consensus clustering, non-intrusive load monitoring, smart buildings, energy disaggregation.

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Non-Intrusive Load Monitoring of Buildings Using Spectral Clustering

Cited by 11 publications

References 53 publications

Learning-Based Non-Intrusive Electric Load Monitoring for Smart Energy Management

Learning-Based Non-Intrusive Electric Load Monitoring for Smart Energy Management

Non-Intrusive Load Monitoring

Accuracy Improvement of Non-Intrusive Load Monitoring Using Voting-Based Consensus Clustering

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