Publications

Journals

  1. Lu, R., Bai, R., Ding, Y., Wei, M., Jiang, J., Sun, M., Xiao, F., & Zhang, H. T. (2021). A hybrid deep learning-based online energy management scheme for industrial microgrid. Applied Energy, 304, 117857. (IF: 9.746)

  2. Lu, R., Bai, R., Luo, Z., Jiang, J., Sun, M., & Zhang, H. T. (2021). Deep Reinforcement Learning-based Demand Response for Smart Facilities Energy Management. IEEE Transactions on Industrial Electronics. (IF: 8.236)

  3. Zhang, X., Lu, R. (Equal Contribution), Jiang, J., Hong, S. H., & Song, W. S. (2021). Testbed implementation of reinforcement learning-based demand response energy management system. Applied Energy, 297, 117131. (IF: 9.746)

  4. Lu, R., Bai, R., Huang, Y., Li, Y., Jiang, J., & Ding, Y. (2021). Data-driven real-time price-based demand response for industrial facilities energy management. Applied Energy, 283, 116291. (IF: 9.746)

  5. Li, Z., Li, Y., Liu, Y., Wang, P., Lu, R., & Gooi, H. B. (2021). Deep learning based densely connected network for load forecasting. IEEE Transactions on Power Systems, 36(4), 2829-2840. (IF: 6.663)

  6. Lu, R., Li, Y. C., Li, Y., Jiang, J., & Ding, Y. (2020). Multi-agent deep reinforcement learning based demand response for discrete manufacturing systems energy management. Applied Energy, 276, 115473. (IF: 8.848)

  7. Zhong, T., Zhang, H. T., Li, Y., Liu, L., & Lu, R. (2020). Bayesian Learning-Based Multi-Objective Distribution Power Network Reconfiguration. IEEE Transactions on Smart Grid, 12(2), 1174-1184. (IF: 10.486)

  8. Lu, R., Hong, S. H., & Yu, M. (2019). Demand Response for Home Energy Management using Reinforcement Learning and Artificial Neural Network. IEEE Transactions on Smart Grid, 10(6), 6629-6639. (IF: 10.486)

  9. Lu, R., & Hong, S. H. (2019). Incentive-based demand response for smart grid with reinforcement learning and deep neural network. Applied Energy, 236, 937-949. (IF: 8.426, ESI Highly Cited)

  10. Lu, R., Hong, S. H., & Zhang, X. (2018). A Dynamic pricing demand response algorithm for smart grid: Reinforcement learning approach. Applied Energy, 220, 220-230. (IF: 7.900, ESI Highly Cited)

  11. Yu, M., Lu R, & Hong, S. H. (2016). A real-time decision model for industrial load management in a smart grid. Applied Energy, 183, 1488-1497. (IF: 7.182)

Conferences

  1. Lu, R., Hong, S. H., Zhang, X., Ye, X., & Song, W. S. (2017, December). A Perspective on Reinforcement Learning in Price-Based Demand Response for Smart Grid. In 2017 International Conference on Computational Science and Computational Intelligence (CSCI) (pp. 1822-1823). IEEE.

  2. Luo, Z., Hong, S., Lu, R., Li, Y., Zhang, X., Kim, J., … & Liang, W. (2017, September). OPC UA-Based Smart Manufacturing: System Architecture, Implementation, and Execution. In 2017 5th international conference on enterprise systems (es) (pp. 281-286). IEEE.

  3. Ding, Y., Hong, S. H., Lu R, Kim, J., Lee, Y. H., Xu, A., & Xiaobing, L. (2015, August). Experimental investigation of the packet loss rate of wireless industrial networks in real industrial environments. In 2015 IEEE International Conference on Information and Automation (pp. 1048-1053). IEEE.

Chinese Journals

Patents