Low-Rate Non-Intrusive Load Disaggregation with Graph Shift Quadratic Form Constraint

Qi, Bing; Liu, Liya; Wu, Xin

doi:10.3390/app8040554

Cited by 16 publications

(7 citation statements)

References 41 publications

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“…The second most common approach defines disaggregation as an optimization problem. Optimization-based methods generally do not rely on events [66]. 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].…”

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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“…NIALM can be built upon signal processing [9], machine learning [4,[9][10][11][12][13][14][15][16][17][18][19][20], and deep learning [20][21][22][23][24]. Not much attention has been paid to evolutionary computing, wherein load disaggregation is considered as a combinatorial optimization problem [1].…”

Section: Introductionmentioning

confidence: 99%

Artificial Intelligence, Accelerated in Parallel Computing and Applied to Nonintrusive Appliance Load Monitoring for Residential Demand-Side Management in a Smart Grid: A Comparative Study

Lin

2020

Applied Sciences

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A smart grid is a promising use-case of AIoT (AI (artificial intelligence) across IoT (internet of things)) that enables bidirectional communication among utilities that arises with demand response (DR) schemes for demand-side management (DSM) and consumers that manage their power demands according to received DR signals. Disaggregating composite electric energy consumption data from a single minimal set of plug-panel current and voltage sensors installed at the electric panel in a practical field of interest, nonintrusive appliance load monitoring (NIALM), a cost-effective load disaggregation approach for (residential) DSM, is able to discern individual electrical appliances concerned without accessing each of them by individual plug-load power meters (smart plugs) deployed intrusively. The most common load disaggregation approaches are based on machine learning algorithms such as artificial neural networks, while approaches based on evolutionary computing, metaheuristic algorithms considered as global optimization and search techniques, have recently caught the attention of researchers. This paper presents a genetic algorithm, developed in consideration of parallel evolutionary computing, and aims to address NIALM, whereby load disaggregation from composite electric energy consumption data is declared as a combinatorial optimization problem and is solved by the algorithm. The algorithm is accelerated in parallel, as it would involve large amounts of NIALM data disaggregated through evolutionary computing, chromosomes, and/or evolutionary cycles to dominate its performance in load disaggregation and excessively cost its execution time. Moreover, the evolutionary computing implementation based on parallel computing, a feed-forward, multilayer artificial neural network that can learn from training data across all available workers of a parallel pool on a machine (in parallel computing) addresses the same NIALM/load disaggregation. Where, a comparative study is made in this paper. The presented methodology is experimentally validated by and applied on a publicly available reference dataset.

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“…However, experimental results show that power fluctuation or a close power range of appliances will influence algorithm performance. Qi adopts graph shift quadratic form constraint to complete low-rate load disaggregation [32]. A novel combined k-means-SVM-based NILM method is developed [33].…”

Section: Introductionmentioning

confidence: 99%

Non-Intrusive Load Disaggregation by Linear Classifier Group Considering Multi-Feature Integration

Gao

et al. 2019

Applied Sciences

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Non-intrusive load monitoring (NILM) is a core technology for demand response (DR) and energy conservation services. Traditional NILM methods are rarely combined with practical applications, and most studies aim to disaggregate the whole loads in a household, which leads to low identification accuracy. In this method, the event detection method is used to obtain the switching event sets of all loads, and the power consumption curves of independent unknown electrical appliances in a period are disaggregated by utilizing comprehensive features. A linear discriminant classifier group based on multi-feature global similarity is used for load identification. The uniqueness of our algorithm is that it designs an event detector based on steady-state segmentation and a linear discriminant classifier group based on multi-feature global similarity. The simulation is carried out on an open source data set. The results demonstrate the effectiveness and high accuracy of the multi-feature integrated classification (MFIC) algorithm by using the state-of-the-art NILM methods as benchmarks.

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Low-Rate Non-Intrusive Load Disaggregation with Graph Shift Quadratic Form Constraint

Cited by 16 publications

References 41 publications

A Scoping Review of Energy Load Disaggregation

A Scoping Review of Energy Load Disaggregation

Artificial Intelligence, Accelerated in Parallel Computing and Applied to Nonintrusive Appliance Load Monitoring for Residential Demand-Side Management in a Smart Grid: A Comparative Study

Non-Intrusive Load Disaggregation by Linear Classifier Group Considering Multi-Feature Integration

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