刘海舟 研究方向: 多能系统优化调度,分布式机器学习 个人网站: https://liuhaizhou.com Email: haizhou501@seu.edu.cn |
个人简介:
刘海舟,博士,2019年本科毕业于南京大学物理学专业,2024年博士毕业于清华大学电气工程专业。曾获本科生国家奖学金、本科优秀毕业生、研究生国家奖学金、博士优秀毕业生等荣誉奖项。于2017年和2023年分别前往美国杜克大学和加州大学伯克利分校交流学习。2024年加入5822yh银河国际5822yh银河国际。
主要从事综合能源系统优化调度、智慧城市用能分析、分布式机器学习、多方数据隐私保护等相关研究课题。
代表文章:
[1]H. Liu, X. Zhang, X. Shen, H. Sun, and M. Shahidehpour, “A hybrid federated learning framework with dynamic task allocation for multi-party distributed load prediction,” IEEE Transactions on Smart Grid, vol. 14, no. 3, pp. 2460-2472, May. 2023.
[2]H. Liu, X. Zhang, H. Sun, and M. Shahidehpour, “Boosted multi-task learning for inter-district collaborative load forecasting,” IEEE Transactions on Smart Grid, vol. 15, no. 1, pp. 973-986, Jan. 2024.
[3] S. Tao*, H. Liu* et al., “Collaborative retired battery sorting for efficient and profitable recycling via federated machine learning,” Nature Communications, vol. 14, Art. No. 8032, Dec. 2023 (*Equal Contribution).
[4]H. Liu, L. Yang, X. Shen, Q. Guo, H. Sun, and M. Shahidehpour, “A data-driven warm start approach for convex relaxation in optimal gas flow,” IEEE Transactions on Power Systems, vol. 36, no. 6, pp. 5948-5951, Nov. 2021.
[5]H. Liu, X. Shen, Q. Guo, and H. Sun, “A data-driven approach towards fast economic dispatch in electricity-gas coupled systems based on artificial neural network,” Applied Energy, vol. 286, Art. No. 116480, Mar. 2021.
[6] H. Liu et al., “Application of modified progressive hedging for stochastic unit commitment in electricity-gas coupled systems,” CSEE Journal of Power and Energy Systems, vol. 7, no. 4, pp. 840-849, Jul. 2021.
[7] Z. Lin*, H. Liu* et al., “Tamm plasmon enabled narrowband thermal emitter for solar thermophotovoltaics,” Solar Energy and Materials and Solar Cells, vol. 238, Art. No. 111589, May 2022 (*Equal Contribution).
[8] X. Liu*, H. Liu*, X. Yu, L. Zhou, and J. Zhu, “Solar thermal utilizations revived by advanced solar evaporation,” Current Opinion in Chemical Engineering, vol. 25, pp. 26-34, Sept. 2019 (*共同一作).