Non-intrusive load decomposition model based on Group Bayesian optimization and post-processing

Non-intrusive load decomposition can decompose the power consumption of a single appliance from the household bus data, which is of great significance for users to adjust their own power consumption strategy. In order to solve the problem of large amount of computation in hyperparameter optimization...

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Bibliographic Details
Published inE3S Web of Conferences Vol. 252; p. 3007
Main Authors Zhukui, Tan, Bin, Liu, Qiuyan, Zhang, Chao, Ding, Houpeng, Hu
Format Journal Article Conference Proceeding
LanguageEnglish
Published Les Ulis EDP Sciences 01.01.2021
Subjects
Bayesian analysis
Computer applications
Decomposition
Optimization
Post-processing
Power consumption
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Summary:Non-intrusive load decomposition can decompose the power consumption of a single appliance from the household bus data, which is of great significance for users to adjust their own power consumption strategy. In order to solve the problem of large amount of computation in hyperparameter optimization of load decomposition model based on deep residual network, a Group Bayesian optimization method is proposed. This method can obtain better hyperparameter combination with less computational cost. In addition, in order to solve the problem of irrelevant activation of the model decomposition results, an improved post-processing method is proposed to improve the comprehensive performance of the model. Finally, the public data set REFIT is used to verify the proposed method, and the results show that the proposed method has a low decomposition error.
ISSN:2267-1242
2555-0403
2267-1242
DOI:10.1051/e3sconf/202125203007