A machine learning-driven virtualized edge framework for real-time monitoring and anomaly detection in solar photovoltaics.

Mamodiya U, Kishor I, Jain J, Jain R

Open source

DOI
10.1038/s41598-026-58923-y
Published
2026 Jun 27
Container
Scientific reports
Publisher
Not recorded
Open access
unknown

Credibility signals

limited evidence Score 43/100 under policy 1.0.0. This is a metadata assessment, not a judgment of the paper's conclusions.

Show all credibility signals

Cite this work

BibTeX

@article{allodium:10.1038/s41598-026-58923-y,
  title = {A machine learning-driven virtualized edge framework for real-time monitoring and anomaly detection in solar photovoltaics.},
  author = {Mamodiya U and Kishor I and Jain J and Jain R},
  year = {2026},
  journal = {Scientific reports},
  doi = {10.1038/s41598-026-58923-y},
  url = {https://doi.org/10.1038/s41598-026-58923-y}
}

RIS

TY  - JOUR
TI  - A machine learning-driven virtualized edge framework for real-time monitoring and anomaly detection in solar photovoltaics.
AU  - Mamodiya U
AU  - Kishor I
AU  - Jain J
AU  - Jain R
PY  - 2026
JO  - Scientific reports
DO  - 10.1038/s41598-026-58923-y
UR  - https://doi.org/10.1038/s41598-026-58923-y
ER  - 

APA

U, M., I, K., J, J., & R, J. (2026). A machine learning-driven virtualized edge framework for real-time monitoring and anomaly detection in solar photovoltaics.. Scientific reports. https://doi.org/10.1038/s41598-026-58923-y

Source records