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

Jiateng Song, Hongbin Wang, Mingxing Du, Lei Peng, Shuai Zhang and Guizhi Xu
Additional contact information
Jiateng Song: State Key Laboratory of Reliability and Intelligence of Electrical Equipment, Hebei University of Technology, Tianjin 300130, China
Hongbin Wang: State Key Laboratory of Reliability and Intelligence of Electrical Equipment, Hebei University of Technology, Tianjin 300130, China
Mingxing Du: State Key Laboratory of Reliability and Intelligence of Electrical Equipment, Hebei University of Technology, Tianjin 300130, China
Lei Peng: State Key Laboratory of Reliability and Intelligence of Electrical Equipment, Hebei University of Technology, Tianjin 300130, China
Shuai Zhang: State Key Laboratory of Reliability and Intelligence of Electrical Equipment, Hebei University of Technology, Tianjin 300130, China
Guizhi Xu: State Key Laboratory of Reliability and Intelligence of Electrical Equipment, Hebei University of Technology, Tianjin 300130, China

Energies, 2021, vol. 14, issue 3, 1-15

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.

Keywords: non-intrusive load monitoring (NILM); long short term memory (LSTM); sequence-to-point (seq2point) learning; load identification (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: 2021
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (4)

Downloads: (external link)
https://www.mdpi.com/1996-1073/14/3/684/pdf (application/pdf)
https://www.mdpi.com/1996-1073/14/3/684/ (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:gam:jeners:v:14:y:2021:i:3:p:684-:d:489079

Access Statistics for this article

Energies is currently edited by Ms. Agatha Cao

More articles in Energies from MDPI
Bibliographic data for series maintained by MDPI Indexing Manager ().