End to end prediction of optimal power flow in sustainable power system using physics-informed spatiotemporal graph neural network
Contents
Data Availability Statement
The source code and dataset are publicly available on GitHub repository; https://github.com/MajidMalori/Optimal-Power-Flow-Prediction-Using-Physics-Informed-Spatiotemporal-Graph-Neural-Network-
License
Copyright (c) 2026 Abdul-Majid Issah Malori, Emmanuel Asuming Frimpong, Francis Effah Boafo , Elvis Twumasi, Peter Asigri, Bernard Marfo Adjei
This work is licensed under a Creative Commons Attribution 4.0 International License.
Authors retain copyright of their work and grant the journal the right to publish it under the Creative Commons Attribution 4.0 International License (CC BY 4.0). This allows for maximum dissemination and reuse with appropriate citation.
How to Cite
Issah Malori, A.-M., Asuming Frimpong, E. ., Effah Boafo , F., Twumasi, E. ., Asigri, P., & Marfo Adjei, B. (2026). End to end prediction of optimal power flow in sustainable power system using physics-informed spatiotemporal graph neural network. Electrical Engineering and Energy, 5(3), 252-282. https://doi.org/10.64470/elene.2026.33
