Publications

  1. S. M. Boroujeni, D. Shi, F. Wang, Y. Zhang, and F. Dadgostari, “Learning Sequential Decision-Making for Distributed Generation Planning in Smart Grids,” in Proc. 2026 IEEE International Conference on Communications, Control, and Computing Technologies for Smart Grids (SmartGridComm), 2026.

  2. Z. Wang, Y. Zhang, L. Wang, and Y. Lin, “Regime-Adaptive Weighted Ensemble Learning for Computing-Driven Dynamic Load Forecasting in AI Data Centers,” in Proc. 2026 IEEE International Conference on Communications, Control, and Computing Technologies for Smart Grids (SmartGridComm), 2026. [Link.]

  3. L. Wang, J. Chen, Y. Zhang, F. Sui, and D. Shi, “Deployment-Efficient Short-Term Load Forecasting in AI Data Centers via Sequence-to-Point Knowledge Distillation,” in Proc. 2026 IEEE International Conference on Communications, Control, and Computing Technologies for Smart Grids (SmartGridComm), 2026. [Link.]

  4. D. Bezborodov, N. Nazemi, M. Revelle, and F. Dadgostari, “Temporal Feature Fusion for Wildfire Segmentation in UAV Video,” in Proc. 2026 IEEE International Geoscience and Remote Sensing Symposium (IGARSS), 2026.

  5. J. Chen, Y. Zhang, L. Wang, and S. Chung, “Physics-Constrained Probabilistic Density Inference for Pseudo-Measurement Generation With Non-Gaussian Uncertainty Modeling,” in Proc. 2026 IEEE Texas Power and Energy Conference (TPEC), 2026.

  6. L. Wang, D. Shi, F. Wang, and K. Sun, “Adaptive In-Context Operator Learning for Fast Power System Dynamic Simulation,” in Proc. 2026 IEEE International Conference on Communications, Control, and Computing Technologies for Smart Grids (SmartGridComm), 2026.

  7. M. F. Soto-Jiménez, E. Jaffer, S. Zhao, S. Soto-Morales, S. Roos-Muñoz, and O. Morton-Bermea, “Sustained Airborne Lead Exposure in an Arid City: The Roles of Legacy Emissions, Meteorology, and Topographic Trapping in Torreón, Mexico,” Air Quality, Atmosphere & Health, 2026. [Link.]

  8. S. Zhao, N. Meyer, B. Hays, and D. Cai, “The Impact of State-Level Policies on Renewable Energy Adoption,” in Proc. Southern Political Science Association Annual Meeting, 2026.

  9. Z. I. Mahmood, Y. Zhang, H. Cui, and A. Ali, “Adaptive Frequency Control for Inverter-Based Resources via Equivalent Inertia-Based Deep Reinforcement Learning,” in Proc. 2026 IEEE Electrical Energy Storage Applications and Technologies Conference (EESAT), Tucson, AZ, USA, 2026. [Link.]

  10. L. Cardiel, S. Chung, and Y. Zhang, “Weighted Ensemble Learning for Short-Term Wind Power Forecasting,” in Proc. the 2026 Dallas Circuits and Systems Conference, 2026.

  11. Y. Zhang, Y. Zhang, and L. Wang, “Meteorological-Data-Aided Distribution System Operation With Weather-Dependent Line Parameter Calibration and DER Generation Forecasting,” IEEE Trans. Ind. Appl., 2026. [Link.]

  12. S. Chung and Y. Zhang, “Utilizing Adversarial Training for Robust Voltage Control: An Adaptive Deep Reinforcement Learning Method,” in Proc. 2026 IEEE Texas Power and Energy Conference (TPEC), 2026. [Link.]

  13. L. Wang, Y. Zhang, D. Shi, F. Ding, “Physics-Aware LLM-Based Probabilistic Wind Power Scenario Generation under Extreme Icing Conditions,” in Proc. 2026 IEEE PES General Meeting, Canada, 2026. [Link.]

  14. Y. Zhang, F. Wang, D. Shi, and C. Fan, “A Distance-Based Spatial-Temporal Simulator for Renewable Energy Forecasting Under Extreme Weather,” in Proc. IEEE PES Transmission & Distribution Conference & Expo (T&D), 2026. [Link.]

  15. R. Kalsher and F. Dadgostari, “AHP and FMEA-Based Electric Grid Risk and Resilience Index: Natural Hazard Risks and Economic Implications for State Electric Grids,” in Proc. 2025 ASEM International Annual Conference, 2025.

  16. C. Fan, M. J. Prothan, Y. Zhu, and D. Shi, “Two Decades of Urban Transformation and Heat Dynamics in a Desert Metropolis: Linking Land Cover, Demographics, and Surface Temperature”, Land, vol. 14, no. 11, p. 2141, Oct. 2025. [Link.]

  17. X. Wang, D. Shi, and F. Wang, “Real-Time Detection and Tracking of Foreign Object Intrusions in Power Systems via Feature-Based Edge Intelligence,” IEEE Open Access Journal of Power and Energy, Sept. 2025. [Link.]

  18. M. Prothan, C. Fan, F. Wang, and D. Shi, “Segmented Wind Power Curve Calibration Using Sub-Hourly Power Bias Analysis,” in Proc. 2024 57th North American Power Symp. (NAPS), pp. 1-6, Oct. 2025. [Link.]

  19. S. Fanifosi, Y. Ye, P. Verma, F. Wang, and D. Shi, “Uncertainty-Aware Deep Reinforcement Learning for Robust Autonomous Voltage Control,” in Proc. 2024 57th North American Power Symp. (NAPS), pp. 1-6, Oct. 2025. [Link.]

  20. T. Vo, L. Hu, L. Xue, and S. Chen, “Trends in Cloud Cover Across CONUS (1980-2020). Journal of Climate, in press.

  21. P. Verma, D. Shi, Y. Ye, F. Wang, and Y. Zhang, “Impact of Solar Integration on Grid Security: Unveiling Vulnerabilities in Load Redistribution Attacks,” in Proc. 2025 Powertech, 2025. [Link.]

  22. Y. Zhang, Y. Wang, Y. Zhang, E. C. Larson, F. Sui, and D. Shi, “On the Potential of Digital Twins for Distribution System State Estimation with Randomly Missing Data in Heterogeneous Measurements,” in Proc. 2025 IEEE PES General Meeting, Austin, TX, pp. 1-5, 2025. [Link.]

  23. Y. Zhang, Y. Zhang, L. Wang, and M. Shahidehpour, “Weather-Dependent Power Flow in Distribution Systems under Extreme Weather: Case Study and Risk Assessment in Wildfire Scenarios,” in Proc. 2025 IEEE PES General Meeting, Austin, TX, pp. 1-5, 2025. [Link.]

  24. Y. Zhang, Y. Zhang, L. Wang, and A. Zhou, “Weather-Dependent Fast Power Flow in Distribution Systems: A Meteorological-Data-Aided Method,” in Proc. 2025 IEEE Texas Power and Energy Conf. (TPEC), 2025. [Link.]

  25. X. Han, M. H. L. Kaas, and C. Wang, “A cross-cultural examination of fairness beliefs in human-AI interaction,” in Ethics of Institutional Beliefs: From Theoretical to Empirical, A. Dyrda, M. Juzaszek, B. Biskup, and C. Wang, Eds., Edward Elgar Publishing, Jan. 2025. [Link.]

  26. S. Chung, Y. Zhang, and Y. Zhang, “Knowledge-Inspired Data-Aided Robust Power Flow in Distribution Networks With ZIP Loads and High DER Penetration,” IEEE Trans. Ind. Appl., vol. 61, no. 1, pp. 1523-1532, Jan.-Feb. 2025. [Link.]

  27. D. Shi, Q. Zhang, M. Hong, F. Wang, S. Maslennikov, X. Luo, and Y. Chen, “Implementing Deep Reinforcement Learning-Based Grid Voltage Control in Real-World Power Systems: Challenges and Insights,” IEEE PES ISGT Europe, 2024. [Link.]

  28. J. Ansu, F. Wang, and D. Shi, “Impact Assessment of Synthetic Inertia and Demand Response on Unit Commitment in Electricity Markets,” in Proc. 2024 56th North American Power Symp. (NAPS), pp. 1-6, 2024. [Link.]