Non-intrusive load monitoring using artificial intelligence classifiers: Performance analysis of machine learning techniques

Monteiro, Raul Vitor Arantes; Santana, Jaqueline Cárdenas; Teixeira, Raoni F. S.; Bretãs, Arturo S.; Aguiar, Rafael L.; Poma, Carlos Enrique Portugal

doi:10.1016/j.epsr.2021.107347

Cited by 17 publications

(6 citation statements)

References 9 publications

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“…Therefore, more research should be performed on the unsupervised learning methods.  In [15,18,19,23,36], authors used the deep learning algorithm to the appliance classification, the results show good performances in the unseen houses. Hence, future works should focus on applying unsupervised learning on deep learning algorithms to achieve better performances.…”

Section: Discussion Challenges and Future Researchmentioning

confidence: 99%

Overview of Non-Intrusive Load Monitoring: Probabilistic and Artificial Intelligence approaches

Ouzine¹,

Marzouq²,

Bennani³

et al. 2022

E3S Web Conf.

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Reduction and conservation of electrical energy consumption in residential buildings is the main objective of Non-Intrusive Load Monitoring (NILM) techniques. NILM detects events and estimate the power consumption of individual appliances by analyzing the aggregate power consumption measured at the service entry. Indeed, our major contribution in this paper is to update research works on NILM methodologies by adding the most recent NILM methods proposed in the literature. In this paper we present an overview of NILM and energy disaggregation methods. Then, we discuss the challenges of this technique to provide useful recommendations for future research.

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Section: Discussion Challenges and Future Researchmentioning

confidence: 99%

Overview of Non-Intrusive Load Monitoring: Probabilistic and Artificial Intelligence approaches

Ouzine¹,

Marzouq²,

Bennani³

et al. 2022

E3S Web Conf.

View full text Add to dashboard Cite

show abstract

“…To evaluate the feasibility of the proposed method for nonintrusive load classification and recognition, we refer to the performance indicators used in [31,32], including accuracy, precision, recall, and F1 score.…”

Section: Performance Indicatorsmentioning

confidence: 99%

Non-Intrusive Load Classification and Recognition Using Soft-Voting Ensemble Learning Algorithm With Decision Tree, K-Nearest Neighbor Algorithm and Multilayer Perceptron

Yang,

Sung

2023

IEEE Access

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Non-intrusive load monitoring (NILM) detects the energy consumption of individual appliances by monitoring the overall electricity usage in a building. By analyzing voltage and current characteristics, NILM can recognize the usage patterns of various appliances, thus facilitating energy conservation and management. To implement non-intrusive load classification and recognition more effectively, this study proposes an ensemble learning algorithm based on soft voting, which comprises a decision tree, K-nearest neighbor algorithm, and multilayer perceptron (EL-SVDT-KNN-MLP). In this study, the voltage and current features in the plug-load appliance identification dataset (PLAID) and worldwide household and industry transient energy dataset (WHITED) are used as input data. The dataset is examined thoroughly and preprocessed before it is fed into the EL-SVDT-KNN-MLP. During preprocessing, six different normalization techniques are applied to the data to improve the accuracy and reliability of the machinelearning model, thus rendering the proposed algorithm more adept at classifying and recognizing appliances. The proposed method is validated by comparing it with other machine learning algorithms in terms of accuracy, precision, recall, and F1 score under the six different normalization methods. The results show that the proposed EL-SVDT-KNN-MLP algorithm outperforms the other ten machine learning algorithms examined in this study.

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“…With the development of machine learning, various optimizations based NILM approaches have been proposed (Monteiro et al, 2021).…”

Section: Related Workmentioning

confidence: 99%

A non-intrusive load decomposition method of resident by multi-scale attention mechanism

Dai

Xie²,

Huang³

et al. 2023

Front. Energy Res.

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Non-intrusive load monitoring (NILM) is one of the important technologies in home energy management and power demand response scenario. However, the presence of multi-mode appliances and appliances with close power values have affected in diminishing the accuracy of identification based NILM algorithms. To tackle these challenges, the work proposes a resident load decomposition method combining multi-scale attention mechanism and convolutional neural network. At the first stage, the attention scores of the normal load data at the previous few moments of the attention model are smoothed dynamically against the abnormal scores at the current moment. The load identification attention model is optimized by constraint factors. Then, on this basis, convolution filters of different sizes are used to model the mixed load data of different electrical equipment, to mine more abundant characteristic information. Finally, to illustrate the proposed processes and validate its effectiveness, taking the PLAID data set as an example, the method proposed in the article is compared with respect to the existing NILM techniques. The experimental results show that the method based on the multi-scale attention mechanism in this paper can greatly improve the effect of load decomposition. Moreover, it reduces the confusion problem of electrical appliance identification with similar load characteristics.

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Non-intrusive load monitoring using artificial intelligence classifiers: Performance analysis of machine learning techniques

Cited by 17 publications

References 9 publications

Overview of Non-Intrusive Load Monitoring: Probabilistic and Artificial Intelligence approaches

Overview of Non-Intrusive Load Monitoring: Probabilistic and Artificial Intelligence approaches

Non-Intrusive Load Classification and Recognition Using Soft-Voting Ensemble Learning Algorithm With Decision Tree, K-Nearest Neighbor Algorithm and Multilayer Perceptron

A non-intrusive load decomposition method of resident by multi-scale attention mechanism

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