AI and ML Methods for Enhancing Power System Operations

Xianbang Chen; Lei Wu

doi:10.53941/aieng.2025.100007

Abstract

With the growing scale and complexity of modern power grids, recent advances in computing technologies, together with the availability of plenty of heterogeneous data, open the door for applying state-of-the-art machine learning approaches to solve critical power system operation and control problems. This Perspective discusses major data-driven machine learning methods and their potential applicability in integrating with physical model-driven approaches to reshape operational practices across modern power systems, followed by the introduction of five contributed articles in the Inaugural Issue of “AI and ML Methods for Enhancing Power System Operations”.

References

1.
Wang, J.; Liu, M.; Yang, N.; et al. Intelligent Scenario Generation for Weekly and Daily Power System Operations under Typhoon Conditions. AI Eng. 2025, 1, 4. https://doi.org/10.53941/aieng.2025.100004.
2.
Li, Y.; Gu, J.; Wu, J.; et al. On Wide-Band Oscillation Localization in Power Transmission Grids: Explainability and Improvement. AI Eng. 2025, 1, 2. https://doi.org/10.53941/aieng.2025.100002.
3.
Bavil, A.; Khodayar, M.; Regan, J.; et al. Dynamic Attention and Context-Aware Feature Fusion for Multi-Scale Solar Panel Defect Detection. AI Eng. 2025, 1, 6. https://doi.org/10.53941/aieng.2025.100006.
4.
Ying, Y.; Zhai, Q.; Zhou, Y.; et al. An LSTM-Driven Efficient Solution Algorithm of Economic Dispatch with Integral Constraints for Energy-Intensive Enterprise Microgrids. AI Eng. 2025, 1, 5. https://doi.org/10.53941/aieng.2025.100005.
5.
Wang, Y.; Li, Q.; Xu, Y. Building Electricity Load Forecasting Considering Climate Change Impacts: A Multi-Factor Deep Learning Approach. AI Eng. 2025, 1, 3. https://doi.org/10.53941/aieng.2025.100003.

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