A physics-informed spatiotemporal graph neural network for photovoltaic power forecasting and micro-anomaly localization
- DOI
- 10.1016/j.nxener.2026.101127
- Published
- 2026-10
- Container
- Next Energy
- Publisher
- Elsevier BV
- Open access
- unknown
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Cite this work
BibTeX
@article{allodium:10.1016/j.nxener.2026.101127,
title = {A physics-informed spatiotemporal graph neural network for photovoltaic power forecasting and micro-anomaly localization},
author = {Mortatha Faleh Majed and Ali S. Abed Al Sailawi and Hassan A. Hameed Al-Hamzawi},
year = {2026},
journal = {Next Energy},
doi = {10.1016/j.nxener.2026.101127},
url = {https://doi.org/10.1016/j.nxener.2026.101127}
}RIS
TY - JOUR TI - A physics-informed spatiotemporal graph neural network for photovoltaic power forecasting and micro-anomaly localization AU - Mortatha Faleh Majed AU - Ali S. Abed Al Sailawi AU - Hassan A. Hameed Al-Hamzawi PY - 2026 JO - Next Energy DO - 10.1016/j.nxener.2026.101127 UR - https://doi.org/10.1016/j.nxener.2026.101127 ER -
APA
Majed, M. F., Sailawi, A. S. A. A., & Al-Hamzawi, H. A. H. (2026). A physics-informed spatiotemporal graph neural network for photovoltaic power forecasting and micro-anomaly localization. Next Energy. https://doi.org/10.1016/j.nxener.2026.101127
Source records
- crossref · retrieved 2026-11-10T14:37:53.074Z