Published in Energy and AI, 2026
Graph neural networks that generalize across grid topologies and operating conditions for AC optimal power flow.
Recommended citation: O. Arowolo et al. (2026). "Towards generalization of graph neural networks for AC optimal power flow." Energy and AI.
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Published in Sustainable Energy, Grids and Networks, 2025
How well do machine learning surrogates for power system time-domain simulation extrapolate beyond their training data?
Recommended citation: O. Arowolo et al. (2025). "Exploring extrapolation of machine learning models for power system time domain simulation." Sustainable Energy, Grids and Networks.
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Published in IEEE Transactions on Sustainable Energy, 2025
Machine learning accelerates ADMM-based decomposition for stochastic power system scheduling with numerous scenarios.
Recommended citation: O. Arowolo et al. (2025). "Learning-Accelerated ADMM for Stochastic Power System Scheduling With Numerous Scenarios." IEEE Transactions on Sustainable Energy.
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Published in International Journal of Computing and Digital Systems, 2021
A computer vision system for recognizing human posture in surveillance settings.
Recommended citation: O. F. Arowolo, E. O. Arogunjo, D. G. Owolabi, and E. D. Markus (2021). "Development of A Human Posture Recognition System for Surveillance Application." International Journal of Computing and Digital Systems. 10(1), 1191–1197.
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Published in IEEE PES Innovative Smart Grid Technologies Conference Europe (ISGT Europe), 2025
Spatio-temporal graph neural networks for the multi-period optimal power flow problem.
Recommended citation: O. Arowolo et al. (2025). "Spatio-Temporal Graph Neural Networks for Multi-Period Optimal Power Flow." 2025 IEEE PES ISGT Europe.
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Published in SSRN preprint, 2026
A tabular foundation model approach to data-driven dynamic security assessment of power systems.
Recommended citation: O. Arowolo et al. (2026). "Revisiting data-driven dynamic security assessment with a tabular foundation model." SSRN.
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Published in arXiv preprint, 2022
Data augmentation techniques for satellite imagery in computer vision pipelines.
Recommended citation: O. Adedeji, P. Owoade, O. Ajayi, and O. Arowolo (2022). "Image Augmentation for Satellite Images." arXiv:2207.14580.
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