A new graph learning-based signal processing approach for non-intrusive load disaggregation with active power measurements

Zhai, Mingyue

doi:10.1007/s00521-019-04623-w

Cited by 5 publications

(3 citation statements)

References 31 publications

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“…Several optimization algorithms exist, such as integer programming used by [67,68], an improved version of the Prey-Predator Optimization Algorithm in [50], and particle swarm optimization in [62,69]. Graph Signal Processing (GSP) is another popular method and can be employed in both supervised and unsupervised settings, e.g., [70][71][72]. For instance [16] apply a supervised GSP algorithm on real data sampled at 15-minute intervals, while Ming-Yue Zhai also uses GSP in an article [36].…”

Section: Related Methodsmentioning

confidence: 99%

A Scoping Review of Energy Load Disaggregation

Tolnai,

Ma,

Jørgensen

2023

Lecture Notes in Computer Science

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Energy load disaggregation can contribute to balancing power grids by enhancing the effectiveness of demand-side management and promoting electricity-saving behavior through increased consumer awareness. However, the field currently lacks a comprehensive overview. To address this gap, this paper conducts a scoping review of load disaggregation domains, data types, and methods, by assessing 72 full-text journal articles. The findings reveal that domestic electricity consumption is the most researched area, while others, such as industrial load disaggregation, are rarely discussed. The majority of research uses relatively low-frequency data, sampled between 1 and 60 seconds. A wide variety of methods are used, and artificial neural networks are the most common, followed by optimization strategies, Hidden Markov Models, and Graph Signal Processing approaches.

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Section: Related Methodsmentioning

confidence: 99%

A Scoping Review of Energy Load Disaggregation

Tolnai,

Ma,

Jørgensen

2023

Lecture Notes in Computer Science

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“…Besides the HMM-based approaches, another probabilistic method that has been used in the NILM field is graph signal processing (GSP) as in [ 24 , 25 , 26 ], mostly all by the same authors. In [ 27 ], the authors highlighted potential problems of the model’s underperformance in real-world practice in the case when training data were lacking, proposing GSP with a novel learning algorithm as an appropriate solution for the NILM problem. Furthermore, in a recent study [ 28 ], GSP, enhanced by improving feature selection through extracting state transition sequence features, showed performance improvement on the publicly available data.…”

Section: Related Workmentioning

confidence: 99%

A Semi-Supervised Approach for Improving Generalization in Non-Intrusive Load Monitoring

Pujić

Tomašević

Batić

2023

Sensors

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Non-intrusive load monitoring (NILM) considers different approaches for disaggregating energy consumption in residential, tertiary, and industrial buildings to enable smart grid services. The main feature of NILM is that it can break down the bulk electricity demand, as recorded by conventional smart meters, into the consumption of individual appliances without the need for additional meters or sensors. Furthermore, NILM can identify when an appliance is in use and estimate its real-time consumption based on its unique consumption patterns. However, NILM is based on machine learning methods and its performance is dependent on the quality of the training data for each appliance. Therefore, a common problem with NILM systems is that they may not generalize well to new environments where the appliances are unknown, which hinders their widespread adoption and more significant contributions to emerging smart grid services. The main goal of the presented research is to apply a domain adversarial neural network (DANN) approach to improve the generalization of NILM systems. The proposed semi-supervised algorithm utilizes both labeled and unlabeled data and was tested on data from publicly available REDD and UK-DALE datasets. The results show a 3% improvement in generalization performance on highly uncorrelated data, indicating the potential for real-world applications.

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“…Gao and Yuan [15] proposed a research on pavement object recognition based on machine learning, including analysis of key technologies of machine learning and optimization of related algorithms. GSP is used by Zhai [16] method to non-intrusive appliance load monitoring (NILM). Wang et al [17] proposed an embedded adaptive cross-modulation (EACM) method for few-shot learning which combines the information between support and query examples.…”

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confidence: 99%

Special issue on Neural Computing and Applications in cyber intelligence: ATCI 2019

Abawajy

2020

Neural Comput & Applic

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A new graph learning-based signal processing approach for non-intrusive load disaggregation with active power measurements

Cited by 5 publications

References 31 publications

A Scoping Review of Energy Load Disaggregation

A Scoping Review of Energy Load Disaggregation

A Semi-Supervised Approach for Improving Generalization in Non-Intrusive Load Monitoring

Special issue on Neural Computing and Applications in cyber intelligence: ATCI 2019

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