徐婷婷

教育背景及工作经历

2026.05～至今    上海第二工业大学 能源与材料学院 讲师

2022.11～2026.4  同济大学       机械与能源学院 博士后

2019.10～2022.03北九州市立大学  国际环境工学专业    博士

2017.04～2019.08北九州市立大学  国际环境工学专业    硕士

2012.09～2016.06上海电力大学    热能与动力工程专业  学士

主讲课程

《人工智能在能源与材料领域的应用》、《能源工程管理》等

研究领域

1. 人工智能与综合能源领域交叉；

2. 综合能源系统优化设计与运行；

3. 新能源消纳及其与电网的交互。

在研项目

1. 中国博士后面上基金；基于多场景下经济效益反馈机制的区域综合能源站设计运行一体化研究，2024-2026，已结项；

编著及参编主要教材专著

Distributed Energy Resources (Solutions for a Low Carbon Society)-Chapter4: The Implementation and Integration of Renewable Energy and Its Impact on thePublic Grid. Springer, Green Energy and Technology, 英文专著，子章节一作

学术及科研成果

无

学术兼职

1. 《Applied Energy》、《Results in Engineering》等期刊审稿人；

荣誉奖励

无

授权专利

无

主要论文

[1] T. T. Xu, Y. T. Yao, F. Y. Qian, Y. J.Ruan, H. Meng, C. L. Wang and W. Liu, Geographic information system andEnergyPLAN-based assessment of pumped hydro storage potential for renewableintegration in the power grid. Energy, 2025. 334: p. 137602.

[2] Y. J. Ruan, T. T. Xu, G. Y. Chen, W. G.Zhou, J. W. Yao, F. Y. Qian, C. Y. Huang and H. Meng, Collaborativeoptimization design for district distributed energy system based on energystation and pipeline network interactions. Sustainable Cities and Society,2023. 103: p. 105017.

[3] T. T. Xu, W. J. Gao, F. Y. Qian and Y. X.Li, The implementation limitation of variable renewable energies and itsimpacts on the public power grid. Energy, 2021. 234: p. 121992.

[4] T. T. Xu, W. J. Gao, Y. X. Li and F. Y.Qian, Impact of the COVID-19 pandemic on the reduction of electricity demandand the integration of renewable energy into the power grid. Journal ofRenewable and Sustainable Energy, 2021. 13(2): p. 026304.

[5] Q. R. Liu, P. F. Zhao, F. Y. Qian, Y. T.Yao, H. Meng, Y. Gao, T. T. Xu* and Y. J. Ruan*, A clustering enhancedgenerative adversarial network approach for addressing limited data anduncertainty in photovoltaic output scenarios. Engineering Applications ofArtificial Intelligence, 2026. 166(B): p. 113647.

[6] Y. J. Ruan, Y. M. Ma, H. Meng, T. T. Xu*and Y. T. Yao* et al., A transfer learning framework using spatiotemporal graphconvolutional network and adversarial domain adaptation for cross-districtbuilding group energy prediction. Building Simulation, 2025.

[7] Y. J. Ruan, Y. M. Ma, H. Meng, T. T. Xu, Y.T. Yao, F. Y. Qian, C. L. Wang and W. Liu, Interpretable multi-feature districtbuilding energy consumption prediction model: Based on dual attention mechanismand spatiotemporal graph convolutional network. Energy and Buildings, 2025.347(B): p. 116365.

[8] D. Yu, T. T. Xu*, Y. X. Jiang*, Q. Li andF. Y. Qian, A flexible quantification method for buildings’ air conditioning based on the light and heattransfer coefficients: A case study of a Shanghai office building. Energies,2025. 18(6): p. 1311.

[9] Q. S. Yang, Y. J. Ruan, F. Y. Qian, H.Meng, Y. T. Yao*, T. T. Xu*, C. L. Wang and W. Liu, Collaborative planning ofregional integrated energy system in the era of EV penetration: A comprehensivereview. Sustainable Cities and Society, 2024. 112: p. 106013.5. 

其他