Non-Intrusive Load Identification Method Based on Improved Long Short Term Memory Network

Jiateng Song; Hongbin Wang; Mingxing Du; Lei Peng; Shuai Zhang; Guizhi Xu

LAPSE:2023.29364

Published Article

LAPSE:2023.29364

Non-Intrusive Load Identification Method Based on Improved Long Short Term Memory Network

Jiateng Song, Hongbin Wang, Mingxing Du, Lei Peng, Shuai Zhang, Guizhi Xu

April 13, 2023

Abstract
Non-intrusive load monitoring (NILM) is an important research direction and development goal on the distribution side of smart grid, which can significantly improve the timeliness of demand side response and users’ awareness of load. Due to rapid development, deep learning becomes an effective way to optimize NILM. In this paper, we propose a novel load identification method based on long short term memory (LSTM) on deep learning. Sequence-to-point (seq2point) learning is introduced into LSTM. The innovative combination of the LSTM and the seq2point brings their respective advantages together, so that the proposed model can accurately identify the load in process of time series data. In this paper, we proved the feature of reducing identification error in the experimental data, from three datasets, UK-DALE dataset, REDD dataset, and REFIT dataset. In terms of mean absolute error (MAE), the three datasets have increased by 15%, 14%, and 18% respectively; in terms of normalized signal aggregate error (SAE), the three datasets have increased by 21%, 24%, and 30% respectively. Compared with the existing models, the proposed model has better accuracy and generalization in identifying three open source datasets.

Record ID

LAPSE:2023.29364

Keywords

load identification, long short term memory (LSTM), non-intrusive load monitoring (NILM), sequence-to-point (seq2point) learning

Subject

System Identification

Suggested Citation

Song J, Wang H, Du M, Peng L, Zhang S, Xu G. Non-Intrusive Load Identification Method Based on Improved Long Short Term Memory Network. (2023). LAPSE:2023.29364

Author Affiliations

Song J: State Key Laboratory of Reliability and Intelligence of Electrical Equipment, Hebei University of Technology, Tianjin 300130, China; Tianjin Key Laboratory of Bioelectromagnetic Technology and Intelligent Health, Hebei University of Technology, Tianjin 30
Wang H: State Key Laboratory of Reliability and Intelligence of Electrical Equipment, Hebei University of Technology, Tianjin 300130, China; Tianjin Key Laboratory of Bioelectromagnetic Technology and Intelligent Health, Hebei University of Technology, Tianjin 30 [ORCID]
Du M: State Key Laboratory of Reliability and Intelligence of Electrical Equipment, Hebei University of Technology, Tianjin 300130, China; Tianjin Key Laboratory of Control Theory & Applications in Complicated System, Tianjin University of Technology, Tianjin 3 [ORCID]
Peng L: State Key Laboratory of Reliability and Intelligence of Electrical Equipment, Hebei University of Technology, Tianjin 300130, China; Tianjin Key Laboratory of Bioelectromagnetic Technology and Intelligent Health, Hebei University of Technology, Tianjin 30
Zhang S: State Key Laboratory of Reliability and Intelligence of Electrical Equipment, Hebei University of Technology, Tianjin 300130, China; Tianjin Key Laboratory of Bioelectromagnetic Technology and Intelligent Health, Hebei University of Technology, Tianjin 30
Xu G: State Key Laboratory of Reliability and Intelligence of Electrical Equipment, Hebei University of Technology, Tianjin 300130, China; Tianjin Key Laboratory of Bioelectromagnetic Technology and Intelligent Health, Hebei University of Technology, Tianjin 30

Journal Name

Energies

Volume

14

Issue

3

First Page

684

Year

2021

Publication Date

2021-01-29