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Non-Intrusive Load Identification Method Based on Improved Long Short Term Memory Network
Content Provider | MDPI |
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Author | Song, Jiateng Wang, Hong Bin Du, Mingxing Peng, Lei Zhang, Shuai Xu, Guizhi |
Copyright Year | 2021 |
Description | 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. |
Starting Page | 684 |
e-ISSN | 19961073 |
DOI | 10.3390/en14030684 |
Journal | Energies |
Issue Number | 3 |
Volume Number | 14 |
Language | English |
Publisher | MDPI |
Publisher Date | 2021-01-29 |
Access Restriction | Open |
Subject Keyword | Energies Industrial Engineering Non-intrusive Load Monitoring (nilm) Long Short Term Memory (lstm) Sequence-to-point (seq2point) Learning Load Identification |
Content Type | Text |
Resource Type | Article |