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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
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
Physics-informed linear regression is competitive with two machine learning methods in residential building MPC
Bünning, F., Huber, B., Schalbetter, A., Aboudonia, A., Hudoba de Badyn, M., Heer, P., … Lygeros, J. (2022). Physics-informed linear regression is competitive with two machine learning methods in residential building MPC. Applied Energy, 310, 118491 (14 pp.). https://doi.org/10.1016/j.apenergy.2021.118491
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
Input convex neural networks for building MPC
Bünning, F., Schalbetter, A., Aboudonia, A., Hudoba de Badyn, M., Heer, P., & Lygeros, J. (2021). Input convex neural networks for building MPC. In A. Jadbabaie, J. Lygeros, G. J. Pappas, P. A. Parrilo, B. Recht, C. J. Tomlin, & M. N. Zeilinger (Eds.), Proceedings of machine learning research: Vol. 144. Learning for dynamics and control (pp. 251-262). ML Research Press.