Multi-Label Classification Based on Random Forest Algorithm for Non-Intrusive Load Monitoring System

Wu, Xin; Gao, Yuchen; Jiao, Dian

doi:10.3390/pr7060337

Cited by 68 publications

(37 citation statements)

References 34 publications

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“…The community has therefore focused on both supervised and unsupervised machine learning techniques. Among the supervised techniques, several neural network architectures have been proposed, such as Multi Layer Perceptron (MLP) [29], Convolutional Neural Network (CNN) [30][31][32][33][34][35][36], Recurrent Neural Network (RNN) [30,[37][38][39], Extreme Learning Machine [40], techniques based on Support Vector Machines (SVM) [16,41], K-Nearest Neighbors (kNN) [41,42] naive Bayes classifiers [15], Random Forest classifier [43] and Conditional Random Fields [44]. Among the unsupervised techniques, it was mainly those based on Hidden Markov Model that were used in this field [26,28,[45][46][47][48], although clustering techniques were also used [49,50].…”

Section: Introductionmentioning

confidence: 99%

Non-Intrusive Load Disaggregation by Convolutional Neural Network and Multilabel Classification

2020

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Non-intrusive load monitoring (NILM) is the main method used to monitor the energy footprint of a residential building and disaggregate total electrical usage into appliance-related signals. The most common disaggregation algorithms are based on the Hidden Markov Model, while solutions based on deep neural networks have recently caught the attention of researchers. In this work we address the problem through the recognition of the state of activation of the appliances using a fully convolutional deep neural network, borrowing some techniques used in the semantic segmentation of images and multilabel classification. This approach has allowed obtaining high performances not only in the recognition of the activation state of the domestic appliances but also in the estimation of their consumptions, improving the state of the art for a reference dataset.

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Section: Introductionmentioning

confidence: 99%

Non-Intrusive Load Disaggregation by Convolutional Neural Network and Multilabel Classification

2020

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“…The first factor is extremely important compared with the second factor 2, 4,6,8 Median between adjacent scales…”

Section: The Ahpmentioning

confidence: 99%

“…They use artificial intelligence technology to build a model from the historical data for fault classification [1]. There are many reported results on the specific content of data-driven fault diagnosis methods [2][3][4].…”

Section: Introductionmentioning

confidence: 99%

Fault Classification Decision Fusion System Based on Combination Weights and an Improved Voting Method

Zeng

Zhou

et al. 2019

Processes

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It is difficult to correctly classify all faults by using only one classifier, and the performance of most classifiers varies under different conditions. In view of this, a new decision fusion system is proposed to solve the problem of fault classification. The proposed decision fusion system is innovative in two aspects: the use of combined weights and a new improved voting method. The combined weights integrate the subjective and objective weights, where the analytic hierarchy process and entropy weight-technique for order performance by similarity to ideal solution are used to determine the subjective and objective weights of different base classifiers under multiple performance evaluation indicators. Moreover, a new improved voting method based on the concept of classifier validity is proposed to increase the accuracy of the decision system. Finally, the method is validated by the Tennessee Eastman benchmark process, and the classification accuracy of the new method is shown to be improved by more than 5.06% compared to the best base classifier.

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“…They are effective in identifying high-power devices, but it is challenging to separate low-power appliances for them due to the possibility of power overlap. Later works extended the steady-state signature to many aspects, such as harmonics [6], current and voltage waveforms [7], voltage-current trajectory [8][9][10], inactive current [11] etc. All of them can disaggregate certain types of appliances effectively.…”

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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Multi-Label Classification Based on Random Forest Algorithm for Non-Intrusive Load Monitoring System

Cited by 68 publications

References 34 publications

Non-Intrusive Load Disaggregation by Convolutional Neural Network and Multilabel Classification

Non-Intrusive Load Disaggregation by Convolutional Neural Network and Multilabel Classification

Fault Classification Decision Fusion System Based on Combination Weights and an Improved Voting Method

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

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