Non-intrusive load monitoring using multi-label classification methods

Ding, Li; Dick, Scott

doi:10.1007/s00202-020-01078-4

Cited by 17 publications

(7 citation statements)

References 60 publications

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“…The adjustable parameters x l i , σ l i , and y l of the k-Means-clustering-hybridized neurofuzzy classifier above can be finetuned through gradient descent, where the updating rules to the three adjustable parameters of the k-Means-clustering-hybridized neuro-fuzzy classifier are provided in Equations ( 4)- (6). In Equation ( 4), η is a tuning constant; f and y denote f (x 0 p ) and y 0 p , respectively; a = ∑ M l=1 b l ; and b l = ∏ n i=1 exp(−(…”

Section: Neuro-fuzzy Classification With K-means Clusteringmentioning

confidence: 99%

“…The k-Means-clustering-hybridized neuro-fuzzy classifier is finetuned by Equations ( 4)- (6). Nevertheless, it is easily fooled by local minima.…”

Section: Neuro-fuzzy Classification With K-means Clusteringmentioning

confidence: 99%

“…Moreover, monitoring relevant electrical appliances in an intrusive style is impractical as the investment costs, including costs for the installation and maintenance of infrastructure, are high [5]. Therefore, energy decomposition, so-called non-intrusive load monitoring [5][6][7][8][9][10], has been developed. Energy decomposition, as part of an EMS, is a process of decomposing composite/circuit-level electric energy consumption into appliance-level electric energy consumption through examining appliance-specific characteristics such as power consumption [6]; this comes with the advantage of requiring no additional plug-level power meters to be deployed for relevant individual electrical appliances while maintaining the investment costs for the energy management system, including its installation and annual maintenance costs, at a minimum.…”

Section: Introductionmentioning

confidence: 99%

“…Therefore, energy decomposition, so-called non-intrusive load monitoring [5][6][7][8][9][10], has been developed. Energy decomposition, as part of an EMS, is a process of decomposing composite/circuit-level electric energy consumption into appliance-level electric energy consumption through examining appliance-specific characteristics such as power consumption [6]; this comes with the advantage of requiring no additional plug-level power meters to be deployed for relevant individual electrical appliances while maintaining the investment costs for the energy management system, including its installation and annual maintenance costs, at a minimum. Energy decomposition can decompose composite/circuit-level electric energy consumption into appliance-level electric energy consumption based on feature data from relevant electrical appliances.…”

Section: Introductionmentioning

confidence: 99%

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Partitional Clustering-Hybridized Neuro-Fuzzy Classification Evolved through Parallel Evolutionary Computing and Applied to Energy Decomposition for Demand-Side Management in a Smart Home

Lin

Gururaj³

2021

Processes

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The key advantage of smart meters over rotating-disc meters is their ability to transmit electric energy consumption data to power utilities’ remote data centers. Besides enabling the automated collection of consumers’ electric energy consumption data for billing purposes, data gathered by smart meters and analyzed through Artificial Intelligence (AI) make the realization of consumer-centric use cases possible. A smart meter installed in a domestic sector of an electrical grid and used for the realization of consumer-centric use cases is located at the entry point of a household/building’s electrical grid connection and can gather composite/circuit-level electric energy consumption data. However, it is not able to decompose its measured circuit-level electric energy consumption into appliance-level electric energy consumption. In this research, we present an AI model, a neuro-fuzzy classifier integrated with partitional clustering and metaheuristically optimized through parallel-computing-accelerated evolutionary computing, that performs energy decomposition on smart meter data in residential demand-side management, where a publicly available UK-DALE (UK Domestic Appliance-Level Electricity) dataset is used to experimentally test the presented model to classify the On/Off status of monitored electrical appliances. As shown in this research, the presented AI model is effective at providing energy decomposition for domestic consumers. Further, energy decomposition can be provided for industrial as well as commercial consumers.

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Section: Neuro-fuzzy Classification With K-means Clusteringmentioning

confidence: 99%

“…The k-Means-clustering-hybridized neuro-fuzzy classifier is finetuned by Equations ( 4)- (6). Nevertheless, it is easily fooled by local minima.…”

Section: Neuro-fuzzy Classification With K-means Clusteringmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Partitional Clustering-Hybridized Neuro-Fuzzy Classification Evolved through Parallel Evolutionary Computing and Applied to Energy Decomposition for Demand-Side Management in a Smart Home

Lin

Gururaj³

2021

Processes

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show abstract

“…Besides, a novel neuro-fuzzy classification approach is proposed in [14] to address the uncertainties in NILM. Furthermore, multi-label classification was proposed in [15] as a solution with the highest potential for NILM problems, and was widely discussed in the following years [16,17]. In addition to classification, other pattern recognition algorithms also draw attention in the field of load disaggregation field.…”

Section: Introductionmentioning

confidence: 99%

An Enhanced Ensemble Approach for Non-Intrusive Energy Use Monitoring Based on Multidimensional Heterogeneity

Liu

Shi

Wang

et al. 2021

Sensors

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Acting as a virtual sensor network for household appliance energy use monitoring, non-intrusive load monitoring is emerging as the technical basis for refined electricity analysis as well as home energy management. Aiming for robust and reliable monitoring, the ensemble approach has been expected in load disaggregation, but the obstacles of design difficulty and computational inefficiency still exist. To address this, an ensemble design integrated with multi-heterogeneity is proposed for non-intrusive energy use disaggregation in this paper. Firstly, the idea of utilizing a heterogeneous design is presented, and the corresponding ensemble framework for load disaggregation is established. Then, a sparse coding model is allocated for individual classifiers, and the combined classifier is diversified by introducing different distance and similarity measures without consideration of sparsity, forming mutually heterogeneous classifiers. Lastly, a multiple-evaluations-based decision process is fine-tuned following the interactions of multi-heterogeneous committees, and finally deployed as the decision maker. Through verifications on both a low-voltage network simulator and a field measurement dataset, the proposed approach is demonstrated to be effective in enhancing load disaggregation performance robustly. By appropriately introducing the heterogeneous design into the ensemble approach, load monitoring improvements are observed with reduced computational burden, which stimulates research enthusiasm in investigating valid ensemble strategies for practical non-intrusive load monitoring implementations.

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Neural Network-Based Load Identification for Residential Electrical Installations. A Review and an Online Experimental Application

Sonck-Martinez,

Rodríguez-Mata,

Medrano-Hermosillo

et al. 2023

Communications in Computer and Information Science

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Non-intrusive load monitoring using multi-label classification methods

Cited by 17 publications

References 60 publications

Partitional Clustering-Hybridized Neuro-Fuzzy Classification Evolved through Parallel Evolutionary Computing and Applied to Energy Decomposition for Demand-Side Management in a Smart Home

Partitional Clustering-Hybridized Neuro-Fuzzy Classification Evolved through Parallel Evolutionary Computing and Applied to Energy Decomposition for Demand-Side Management in a Smart Home

An Enhanced Ensemble Approach for Non-Intrusive Energy Use Monitoring Based on Multidimensional Heterogeneity

Neural Network-Based Load Identification for Residential Electrical Installations. A Review and an Online Experimental Application

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