Smart meter health state prediction based on residual-connected self-attention ConvLSTM network

Aibeiduo Li, Ting Yang

Open source

DOI: 10.1038/s41598-026-54371-w
Published: 2026-06-03
Container: Scientific Reports
Publisher: Springer Science and Business Media LLC
Open access: unknown

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Cite this work

BibTeX

@article{allodium:10.1038/s41598-026-54371-w,
  title = {Smart meter health state prediction based on residual-connected self-attention ConvLSTM network},
  author = {Aibeiduo Li and Ting Yang},
  year = {2026},
  journal = {Scientific Reports},
  doi = {10.1038/s41598-026-54371-w},
  url = {https://doi.org/10.1038/s41598-026-54371-w}
}

RIS

TY  - JOUR
TI  - Smart meter health state prediction based on residual-connected self-attention ConvLSTM network
AU  - Aibeiduo Li
AU  - Ting Yang
PY  - 2026
JO  - Scientific Reports
DO  - 10.1038/s41598-026-54371-w
UR  - https://doi.org/10.1038/s41598-026-54371-w
ER  -

APA

Li, A., & Yang, T. (2026). Smart meter health state prediction based on residual-connected self-attention ConvLSTM network. Scientific Reports. https://doi.org/10.1038/s41598-026-54371-w

Source records

crossref · retrieved 2026-09-26T05:31:06.803Z