马靖寰 助理教授 硕士生导师
研究领域
电力系统新理论 基于储能的配电网结构与配电规划 电力市场 电力数据分析
联系方式
电子信箱：mjhdtc@sjtu.edu.cn
通信地址：上海市东川路 800 号智能电网中心200-2室
邮编：200240
教育背景
2009.09-2013.07 北京大学 理学学士
2013.09-2018.07 北京大学 工学博士
工作经历
2018.11-今 电子信息与电气工程学院信息技术与电气工程研究院 助理教授
主讲课程
EE425 微机控制技术 （本科生）
GS00001 学术写作、规范及伦理（研究生）
主持科研项目
上海市科学技术委员会2019年上海市青年科技英才扬帆计划：基于大数据技术的电力数据分析与时间序列预测方法研究，2019.05-2022.04
代表作
专题一：能源互联网中基于直流电力包传输的配电网结构和配电
[1] 马靖寰，能源互联网中基于直流电力包传输的配电网结构和运行机制研究，北京大学博士研究生毕业论文，2018.04.
[2] J. Ma et al., "Optimal power dispatching for local area packetized power network," IEEE Transactions on Smart Grid, vol. 9, no. 5, pp. 4765-4776, Sept. 2018.
[3] J. Ma, "Rudiment of energy internet: coordinated power dispatching of intra- and inter-local area packetised-power networks," IET Smart Grid, vol. 2, no. 2, pp. 214-223, 6 2019.
[4] J. Ma et al., "Elastic energy distribution of local area packetized power networks to mitigate distribution level load fluctuation," IEEE Access, vol. 6, pp. 8219-8231, 2018.
专题二：电力系统需求侧管理
[5] J. Ma et al., “Residential load scheduling in smart grid: a cost efficiency perspective”, IEEE Transactions on Smart Grid, vol. 7, no. 2, pp. 771-784, Mar. 2016.
[6] J. Ma et al., “Incentive mechanism for demand side management in smart grid using auction”, IEEE Transactions on Smart Grid, vol. 5, no. 3, pp. 1379-1388, May 2014.
专题三：PEV, PHEV充电站网络与配电规划
[7] N. Chen, J. Ma (correspondence), M. Wang and X. Shen, "Two-tier energy compensation framework based on mobile vehicular electric storage," IEEE Transactions on Vehicular Technology, vol. 67, no. 12, pp. 11719-11732, Dec. 2018.
[8] N. Chen, J. Ma (correspondence), M. Li, M. Wang and X. S. Shen, "Energy management framework for mobile vehicular electric storage," IEEE Network, vol. 33, no. 6, pp. 148-155, Nov.-Dec. 2019.
专题四：电力数据分析
[9] L. Cai, J. Gu, J. Ma (correspondence), and Z. Jin, “Probabilistic wind power forecasting approach via instance-based transfer learning embedded gradient boosting decision trees”, Energies, vol. 12, no. 1, p. 159, Jan. 2019.
[10] H. Wen, J. Gu, J. Ma and Z. Jin, "Probabilistic wind power forecasting via Bayesian deep learning based prediction intervals," 2019 IEEE 17th International Conference on Industrial Informatics (INDIN), Helsinki, Finland, 2019, pp. 1091-1096.