| Computationally efficient reinforcement learning: targeted exploration leveraging simple rules
Di Natale, L., Svetozarevic, B., Heer, P., & Jones, C. N. (2024). Computationally efficient reinforcement learning: targeted exploration leveraging simple rules. In Proceedings of the IEEE conference on decision and control (CDC) (pp. 2334-2339). https://doi.org/10.1109/CDC49753.2023.10384283 |
| Stable linear subspace identification: a machine learning approach
Di Natale, L., Zakwan, M., Svetozarevic, B., Heer, P., Ferrari-Trecate, G., & Jones, C. N. (2024). Stable linear subspace identification: a machine learning approach. In 2024 European Control Conference (ECC) (pp. 3539-3544). https://doi.org/10.23919/ECC64448.2024.10590843 |
| Data-driven adaptive building thermal controller tuning with constraints: a primal–dual contextual Bayesian optimization approach
Xu, W., Svetozarevic, B., Di Natale, L., Heer, P., & Jones, C. N. (2024). Data-driven adaptive building thermal controller tuning with constraints: a primal–dual contextual Bayesian optimization approach. Applied Energy, 358, 122493 (13 pp.). https://doi.org/10.1016/j.apenergy.2023.122493 |
| Computationally efficient reinforcement learning: targeted exploration leveraging simple rules
Di Natale, L., Svetozarevic, B., Heer, P., & Jones, C. N. (2023). Computationally efficient reinforcement learning: targeted exploration leveraging simple rules. In Proceedings of the IEEE conference on decision and control. IEEE conference on decision and control (pp. 2334-2339). https://doi.org/10.1109/CDC49753.2023.10383956 |
| Towards scalable physically consistent neural networks: an application to data-driven multi-zone thermal building models
Di Natale, L., Svetozarevic, B., Heer, P., & Jones, C. N. (2023). Towards scalable physically consistent neural networks: an application to data-driven multi-zone thermal building models. Applied Energy, 340, 121071 (16 pp.). https://doi.org/10.1016/j.apenergy.2023.121071 |
| Lessons learned from data-driven building control experiments: contrasting Gaussian process-based MPC, bilevel deePC, and deep reinforcement learning
Di Natale, L., Lian, Y., Maddalena, E. T., Shi, J., & Jones, C. N. (2022). Lessons learned from data-driven building control experiments: contrasting Gaussian process-based MPC, bilevel deePC, and deep reinforcement learning. In Proceedings of the IEEE conference on decision and control (pp. 1111-1117). https://doi.org/10.1109/CDC51059.2022.9992445 |
| Near-optimal deep reinforcement learning policies from data for zone temperature control
Di Natale, L., Svetozarevic, B., Heer, P., & Jones, C. N. (2022). Near-optimal deep reinforcement learning policies from data for zone temperature control. In IEEE international conference on control and automation. 2022 IEEE 17th international conference on control and automation (ICCA) (pp. 698-703). https://doi.org/10.1109/ICCA54724.2022.9831914 |
| Physically consistent neural networks for building thermal modeling: theory and analysis
Di Natale, L., Svetozarevic, B., Heer, P., & Jones, C. N. (2022). Physically consistent neural networks for building thermal modeling: theory and analysis. Applied Energy, 325, 119806 (17 pp.). https://doi.org/10.1016/j.apenergy.2022.119806 |
| Data-driven control of room temperature and bidirectional EV charging using deep reinforcement learning: simulations and experiments
Svetozarevic, B., Baumann, C., Muntwiler, S., Di Natale, L., Zeilinger, M. N., & Heer, P. (2022). Data-driven control of room temperature and bidirectional EV charging using deep reinforcement learning: simulations and experiments. Applied Energy, 307, 118127 (16 pp.). https://doi.org/10.1016/j.apenergy.2021.118127 |
| Deep Reinforcement Learning for room temperature control: a black-box pipeline from data to policies
Di Natale, L., Svetozarevic, B., Heer, P., & Jones, C. N. (2021). Deep Reinforcement Learning for room temperature control: a black-box pipeline from data to policies. In J. L. Scartezzini & B. Smith (Eds.), Journal of physics: conference series: Vol. 2042. CISBAT 2021 carbon neutral cities - energy efficiency & renewables in the digital era (p. 012004 (6 pp.). https://doi.org/10.1088/1742-6596/2042/1/012004 |
| The potential of vehicle-to-grid to support the energy transition: a case study on Switzerland
Di Natale, L., Funk, L., Rüdisüli, M., Svetozarevic, B., Pareschi, G., Heer, P., & Sansavini, G. (2021). The potential of vehicle-to-grid to support the energy transition: a case study on Switzerland. Energies, 14(16), 4812 (24 pp.). https://doi.org/10.3390/en14164812 |
