Non-Intrusive Load Decomposition Based on Instance-Batch Normalization Networks

Mao Wang, Dandan Liu () and Changzhi Li
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Mao Wang: College of Electronics and Information Engineering, Shanghai University of Electric Power, No. 1851, Hucheng Ring Road, Pudong New Area District, Shanghai 201306, China
Dandan Liu: College of Electronics and Information Engineering, Shanghai University of Electric Power, No. 1851, Hucheng Ring Road, Pudong New Area District, Shanghai 201306, China
Changzhi Li: College of Electronics and Information Engineering, Shanghai University of Electric Power, No. 1851, Hucheng Ring Road, Pudong New Area District, Shanghai 201306, China

Energies, 2023, vol. 16, issue 7, 1-15

Abstract: At present, the non-intrusive load decomposition method for low-frequency sampling data is as yet insufficient within the context of generalization performance, failing to meet the decomposition accuracy requirements when applied to novel scenarios. To address this issue, a non-intrusive load decomposition method based on instance-batch normalization network is proposed. This method uses an encoder-decoder structure with attention mechanism, in which skip connections are introduced at the corresponding layers of the encoder and decoder. In this way, the decoder can reconstruct a more accurate power sequence of the target. The proposed model was tested on two public datasets, REDD and UKDALE, and the performance was compared with mainstream algorithms. The results show that the F 1 score was higher by an average of 18.4 when compared with mainstream algorithms. Additionally, the mean absolute error reduced by an average of 25%, and the root mean square error was reduced by an average of 22%.

Keywords: non-intrusive load monitoring; instance-batch normalization network; attention mechanism; skip connection; transfer learning (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2023
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