Open Access
| Issue |
EPJ Web Conf.
Volume 354, 2026
19th Global Congress on Manufacturing and Management (GCMM 2025)
|
|
|---|---|---|
| Article Number | 02005 | |
| Number of page(s) | 9 | |
| Section | Artificial Intelligence, Machine Learning, and Intelligent Decision Systems | |
| DOI | https://doi.org/10.1051/epjconf/202635402005 | |
| Published online | 02 March 2026 | |
- L. Yu, W. Xie, D. Xie, Y. Zou, D. Zhang, Z. Sun, L. Zhang, Y. Zhang, and T. Jiang, "Deep reinforcement learning for smart home energy management," IEEE Internet of Things Journal, vol. 7, no. 4, pp. 2751–2762, 2019. [Google Scholar]
- H. Karimi, M. A. Adibhesami, H. Bazazzadeh, and S. Movafagh, "Green buildings: Human-centered and energy efficiency optimization strategies," Energies, vol. 16, no. 9, p. 3681, 2023. [Google Scholar]
- L. Yu, S. Qin, M. Zhang, C. Shen, T. Jiang, and X. Guan, "A review of deep reinforcement learning for smart building energy management," IEEE Internet of Things Journal, vol. 8, no. 15, pp. 12046–12063, 2021. [Google Scholar]
- A. Molderink, V. Bakker, M. G. Bosman, J. L. Hurink, and G. J. Smit, "Domestic energy management methodology for optimizing efficiency in smart grids," in Proc. IEEE Bucharest PowerTech, 2009, pp. 1–7. [Google Scholar]
- S. Barker, A. Mishra, D. Irwin, E. Cecchet, P. Shenoy, and J. Albrecht, "Smart*: An open data set and tools for enabling research in sustainable homes," in SustKDD, Aug. 2012, pp. 108–112. [Google Scholar]
- M. Diyan, B. N. Silva, and K. Han, "A multi-objective approach for optimal energy management in smart home using the reinforcement learning," Sensors, vol. 20, no. 12, p. 3450, 2020. [Google Scholar]
- Y. Liu, D. Zhang, and H. B. Gooi, "Optimization strategy based on deep reinforcement learning for home energy management," CSEE Journal of Power and Energy Systems, vol. 6, no. 3, pp. 572–582, 2020. [Google Scholar]
- S.-J. Chen, W.-Y. Chiu, and W.-J. Liu, "User preference-based demand response for smart home energy management using multiobjective reinforcement learning," IEEE Access, vol. 9, pp. 161627–161637, 2021. [Google Scholar]
- T. Wei, Y. Wang, and Q. Zhu, "Deep reinforcement learning for building HVAC control," in Proc. 54th Annual Design Automation Conference (DAC), 2017, pp. 1–6. [Google Scholar]
- O. Alqaryouti, N. Siyam, A. Abdel Monem, and K. Shaalan, "Aspect-based sentiment analysis using smart government review data," Applied Computing and Informatics, vol. 20, no. 1-2, pp. 142–161, 2024. [Google Scholar]
- J. Duan, Z. Chen, F. Zhang, and J. Li, "Multi-objective deep reinforcement learning for intelligent energy management," Applied Energy, vol. 286, p. 116491, 2021. [Google Scholar]
- H. Zhang, Y. Li, and D. Wang, "Preference-based multi-objective reinforcement learning with dynamic scalarization," Neurocomputing, vol. 452, pp. 75–87, 2021. [Google Scholar]
- S. Yang, J. Liu, and X. Chen, "Fairness-aware multi-agent reinforcement learning for resource allocation," IEEE Transactions on Neural Networks and Learning Systems, vol. 33, no. 10, pp. 5560–5573, 2022. [Google Scholar]
- A. Gupta, R. Singh, and P. Kumar, "User-centric deep reinforcement learning for personalized control systems," IEEE Access, vol. 9, pp. 148233–148245, 2021. [Google Scholar]
- Y. Zhao, Q. Wu, and Z. Wang, "Adaptive neural preference modeling using deep representation learning," Expert Systems with Applications, vol. 188, p. 115987, 2022. [Google Scholar]
- L. Chen and H. Xu, "Multi-agent reinforcement learning with attention mechanisms for cooperative decision making," IEEE Transactions on Cybernetics, vol. 53, no. 4, pp. 2451–2463, 2023. [Google Scholar]
- M. R. Islam, T. Rahman, and M. S. Hossain, "Deep neural preference learning for smart environment control," Applied Soft Computing, vol. 129, p. 109603, 2023. [Google Scholar]
- P. Sharma and V. Aggarwal, "Meta-learning based reinforcement learning for personalized adaptive systems," Pattern Recognition Letters, vol. 168, pp. 21–29, 2023. [Google Scholar]
- R. Kumar and S. S. Iyengar, "Multi-objective actor-critic reinforcement learning for dynamic preference optimization," Neural Computing and Applications, vol. 35, pp. 16273–16287, 2023. [Google Scholar]
- X. Liu, Y. Tang, and K. Zhang, "Scalable multi-user reinforcement learning with neural aggregation mechanisms," IEEE Transactions on Artificial Intelligence, vol. 5, no. 1, pp. 132–144, 2024. [Google Scholar]
Current usage metrics show cumulative count of Article Views (full-text article views including HTML views, PDF and ePub downloads, according to the available data) and Abstracts Views on Vision4Press platform.
Data correspond to usage on the plateform after 2015. The current usage metrics is available 48-96 hours after online publication and is updated daily on week days.
Initial download of the metrics may take a while.

