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Data-driven predictive control for demand side management: theoretical and experimental results
Yin, M., Cai, H., Gattiglio, A., Khayatian, F., Smith, R. S., & Heer, P. (2024). Data-driven predictive control for demand side management: theoretical and experimental results. Applied Energy, 353, 122101 (12 pp.). https://doi.org/10.1016/j.apenergy.2023.122101
Comparison of online and offline deep reinforcement learning with model predictive control for thermal energy management
Brandi, S., Fiorentini, M., & Capozzoli, A. (2022). Comparison of online and offline deep reinforcement learning with model predictive control for thermal energy management. Automation in Construction, 135, 104128 (15 pp.). https://doi.org/10.1016/j.autcon.2022.104128
On the use of conditional TimeGAN to enhance the robustness of a reinforcement learning agent in the building domain
Fochesato, M., Khayatian, F., Fonseca Lima, D., & Nagy, Z. (2022). On the use of conditional TimeGAN to enhance the robustness of a reinforcement learning agent in the building domain. In BuildSys '22. The 9th ACM international conference on systems for energy-efficient buildings, cities, and transportation (pp. 208-217). https://doi.org/10.1145/3563357.3564080