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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
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
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