| 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 |
| 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 |
| 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 |
| Heat transfer constraints and performance mapping of a closed liquid sorption heat storage process
Fumey, B., Weber, R., & Baldini, L. (2023). Heat transfer constraints and performance mapping of a closed liquid sorption heat storage process. Applied Energy, 335, 120755 (11 pp.). https://doi.org/10.1016/j.apenergy.2023.120755 |
| Battery-H<sub>2</sub> storage system for self-sufficiency in residential buildings under different electric heating system scenarios
Go, J., Byun, J., Orehounig, K., & Heo, Y. (2023). Battery-H2 storage system for self-sufficiency in residential buildings under different electric heating system scenarios. Applied Energy, 337, 120742 (16 pp.). https://doi.org/10.1016/j.apenergy.2023.120742 |
| Impact of forecast uncertainty and electricity markets on the flexibility provision and economic performance of highly-decarbonized multi-energy systems
Srinivasan, A., Wu, R., Heer, P., & Sansavini, G. (2023). Impact of forecast uncertainty and electricity markets on the flexibility provision and economic performance of highly-decarbonized multi-energy systems. Applied Energy, 338, 120825 (16 pp.). https://doi.org/10.1016/j.apenergy.2023.120825 |
| Platform-based design for energy systems
Sulzer, M., Wetter, M., Mutschler, R., & Sangiovanni-Vincentelli, A. (2023). Platform-based design for energy systems. Applied Energy, 352, 121955 (16 pp.). https://doi.org/10.1016/j.apenergy.2023.121955 |
| Approximating optimal building retrofit solutions for large-scale retrofit analysis
Thrampoulidis, E., Hug, G., & Orehounig, K. (2023). Approximating optimal building retrofit solutions for large-scale retrofit analysis. Applied Energy, 333, 120566 (22 pp.). https://doi.org/10.1016/j.apenergy.2022.120566 |
| Strategic PV expansion and its impact on regional electricity self-sufficiency: case study of Switzerland
Walch, A., & Rüdisüli, M. (2023). Strategic PV expansion and its impact on regional electricity self-sufficiency: case study of Switzerland. Applied Energy, 346, 121262 (16 pp.). https://doi.org/10.1016/j.apenergy.2023.121262 |
| Zinc carboxylate optimization strategy for extending Al-air battery system's lifetime
Wei, M., Wang, K., Pei, P., Zhong, L., Züttel, A., Pham, T. H. M., … Zhao, S. (2023). Zinc carboxylate optimization strategy for extending Al-air battery system's lifetime. Applied Energy, 350, 121804 (11 pp.). https://doi.org/10.1016/j.apenergy.2023.121804 |
| 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 |
| 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 |
| Resilient cooling through geothermal district energy system
Gautier, A., Wetter, M., & Sulzer, M. (2022). Resilient cooling through geothermal district energy system. Applied Energy, 325, 119880 (17 pp.). https://doi.org/10.1016/j.apenergy.2022.119880 |
| Smart power-to-gas deployment strategies informed by spatially explicit cost and value models
Gupta, R., Rüdisüli, M., Patel, M. K., & Parra, D. (2022). Smart power-to-gas deployment strategies informed by spatially explicit cost and value models. Applied Energy, 327, 120015 (13 pp.). https://doi.org/10.1016/j.apenergy.2022.120015 |
| Performance and dynamics of active greywater heat recovery in buildings
Hadengue, B., Morgenroth, E., Larsen, T. A., & Baldini, L. (2022). Performance and dynamics of active greywater heat recovery in buildings. Applied Energy, 305, 117677 (13 pp.). https://doi.org/10.1016/j.apenergy.2021.117677 |
| Quantifying the climate and human-system-driven uncertainties in energy planning by using GANs
Perera, A. T. D., Khayatian, F., Eggimann, S., Orehounig, K., & Halgamuge, S. (2022). Quantifying the climate and human-system-driven uncertainties in energy planning by using GANs. Applied Energy, 328, 120169 (12 pp.). https://doi.org/10.1016/j.apenergy.2022.120169 |
| MANGOret: an optimization framework for the long-term investment planning of building multi-energy system and envelope retrofits
Petkov, I., Mavromatidis, G., Knoeri, C., Allan, J., & Hoffmann, V. H. (2022). MANGOret: an optimization framework for the long-term investment planning of building multi-energy system and envelope retrofits. Applied Energy, 314, 118901 (32 pp.). https://doi.org/10.1016/j.apenergy.2022.118901 |
| Evaluation of the impact of input uncertainty on urban building energy simulations using uncertainty and sensitivity analysis
Prataviera, E., Vivian, J., Lombardo, G., & Zarrella, A. (2022). Evaluation of the impact of input uncertainty on urban building energy simulations using uncertainty and sensitivity analysis. Applied Energy, 311, 118691 (19 pp.). https://doi.org/10.1016/j.apenergy.2022.118691 |
| Prospective life-cycle assessment of greenhouse gas emissions of electricity-based mobility options
Rüdisüli, M., Bach, C., Bauer, C., Beloin-Saint-Pierre, D., Elber, U., Georges, G., … Teske, S. L. (2022). Prospective life-cycle assessment of greenhouse gas emissions of electricity-based mobility options. Applied Energy, 306, 118065 (20 pp.). https://doi.org/10.1016/j.apenergy.2021.118065 |
| 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 |