| Digitalization of urban multi-energy systems – Advances in digital twin applications across life-cycle phases
Koirala, B., Cai, H., Khayatian, F., Munoz, E., An, J. G., Mutschler, R., … Orehounig, K. (2024). Digitalization of urban multi-energy systems – Advances in digital twin applications across life-cycle phases. Advances in Applied Energy, 16, 100196 (22 pp.). https://doi.org/10.1016/j.adapen.2024.100196 |
| 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 |
| Integrated assessment of buildings visual and thermal performance with translucent bricks
Hassoun, L., Khayatian, F., Ganobjak, M., Wernery, J., & Vivian, J. (2023). Integrated assessment of buildings visual and thermal performance with translucent bricks. In M. Andersen, B. Smith, Y. Schwartz, C. Waibel, D. Lindelof, D. Lindelof, & D. Lindelof (Eds.), Journal of physics: conference series: Vol. 2600. Daylighting & electric lighting (p. 112008 (6 pp.). https://doi.org/10.1088/1742-6596/2600/11/112008 |
| Data anonymization and open sharing are key to a sustainable built environment
Khayatian, F. (2023). Data anonymization and open sharing are key to a sustainable built environment. In T. Zhou, Y. Chen, W. Deng, & A. Cheshmehzangi (Eds.), Urban Sustainability. Smart buildings and technologies for sustainable cities in China (pp. 33-45). https://doi.org/10.1007/978-981-99-6391-1_4 |
| Phase change material (PCM) integrations into buildings in hot climates with simulation access for energy performance and thermal comfort: a review
Zhan, H., Mahyuddin, N., Sulaiman, R., & Khayatian, F. (2023). Phase change material (PCM) integrations into buildings in hot climates with simulation access for energy performance and thermal comfort: a review. Construction and Building Materials, 397, 132312 (37 pp.). https://doi.org/10.1016/j.conbuildmat.2023.132312 |
| 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 |
| A data-driven approach for window opening predictions in non-air-conditioned buildings
Fu, Y., Zhou, T., Lun, I., Khayatian, F., Deng, W., & Su, W. (2022). A data-driven approach for window opening predictions in non-air-conditioned buildings. Intelligent Buildings International, 14(3), 329-345. https://doi.org/10.1080/17508975.2021.1963651 |
| 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 |
| Experiment strategy for evaluating advanced building energy management system
Cai, H., Khayatian, F., & Heer, P. (2021). Experiment strategy for evaluating advanced building energy management system. 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. 012030 (6 pp.). https://doi.org/10.1088/1742-6596/2042/1/012030 |
| Temporal resolution of measurements and the effects on calibrating building energy models
Khayatian, F., Bollinger, A., & Heer, P. (2021). Temporal resolution of measurements and the effects on calibrating building energy models. In eSIM 2020 conference proceedings (p. (8 pp.). |
| Using generative adversarial networks to evaluate robustness of reinforcement learning agents against uncertainties
Khayatian, F., Nagy, Z., & Bollinger, A. (2021). Using generative adversarial networks to evaluate robustness of reinforcement learning agents against uncertainties. Energy and Buildings, 251, 111334 (13 pp.). https://doi.org/10.1016/j.enbuild.2021.111334 |
| Unsupervised learning for feature projection: extracting patterns from multidimensional building measurements
Xiao, C., Khayatian, F., & Dall'O', G. (2020). Unsupervised learning for feature projection: extracting patterns from multidimensional building measurements. Energy and Buildings, 224, 110228 (18 pp.). https://doi.org/10.1016/j.enbuild.2020.110228 |