[Selected Journal Papers]
[9] G. Tian, Q,Z, Sun, “A Stochastic Controller for Primary Frequency Regulation using ON/OFF Demand Side Resources,” IEEE Transactions on Smart Grid, 2023.
[8] G. Tian, Q,Z, Sun, and W. Wang, “Real-time Flexibility Quantification of a Building HVAC System for Peak Demand Reduction,” IEEE Transactions on Power Systems, 2022.
[7] W. Wang, G. Tian, Q.Z. Sun, “A Control Framework to Enable a Commercial Building HVAC System for Energy and Regulation Market Signal Tracking,” IEEE Transactions on Power Systems, 2022.
[6] S.G. Faddel, Q. Zhou, G. Tian, “Modeling and Coordination of Commercial Buildings in Distribution Systems,” IEEE Transactions on Industry Applications, vol. 58, no. 2, pp. 1654-1663 2022.
[5] G. Tian, Y. Gu, Z. Yu, Q. Zhang, D. Shi, Q. Zhou, Z. Wang, “Enhanced Denoising Autoencoder Aided Bad Data Filtering for Synchrophasor-based State Estimation,” CSEE Journal of Power and Energy Systems, vol. 8, no. 2, pp. 640-651, 2022.
[4] G. Tian, Y. Gu, D. Shi, J. Fu, Z. Yu, Q. Zhou, “Neural-network-based Power System State Estimation with Extended Observability,” Journal of Modern Power Systems and Clean Energy, vol. 9, no. 5, pp. 5497--5508, 2021.
[3] W. Wang, Q. Zhou, G. Tian, Y. Wang, Z. Zhao, F. Cao, “A Novel Defrosting Initiation Strategy based on Convolutional Neural Network for Air-Source Heat Pump,” International Journal of Refrigeration, vol. 128, pp. 95--103, 2021.
[2] S. Faddel, G. Tian, Q. Zhou, “Decentralized Management of Commercial HVAC Systems,” Energies, vol. 14, no. 11, pp. 3024, 2021.
[1] G. Tian, Q. Zhou, R. Birari, J. Qi, Z. Qu, “A Hybrid-Learning Algorithm for Online Dynamic State Estimation in Multimachine Power Systems,” IEEE Transactions on Neural Network and Learning Systems, vol. 31, no. 12, pp. 5497--5508, 2020.
[Selected Conference Papers]
[6] G. Tian, Q.Z. Sun, “Optimal HVAC Scheduling under Temperature Uncertainty using the Wasserstein Metric,” in 2022 IEEE Power & Energy Society General Meeting (PESGM).
[5] G. Tian, Q.Z. Sun, “Chance Constrained Distributionally Robust Optimal HVAC Scheduling for Commercial Building Demand Response,” in 2022 IEEE North American Innovative Smart Grid Technologies (ISGTNA).
[4] G. Tian, S. Faddel, X. Jin, Q. Zhou, “Probabilistic Power Consumption Modeling for Commercial Buildings Using Logistic Regression Markov Chain,” in 2020 IEEE Power & Energy Society General Meeting (PESGM). (Best Paper Award)
[3] G. Tian, Y. Gu, X. Lu, D. Shi, Q. Zhou, Z. Wang, J. Li, “Estimation Matrix Calibration of PMU Data-driven State Estimation Using Neural Network,” in 2020 IEEE Power & Energy Society General Meeting (PESGM).
[2] G. Tian, S. Faddel, Q. Zhou, Z. Qu, A. Parlato, “Optimal Coordination of HVAC Scheduling for Commercial Buildings,” in 2020 IEEE Texas Power and Energy Conference (TPEC).
[1] G. Tian, Q. Zhou, L. Du, “Deep Convolutional Neural Networks for Distribution System Fault Classification,” in 2018 IEEE Power & Energy Society General Meeting (PESGM).