1 code implementation • 31 Mar 2024 • Chun Fu, Hussain Kazmi, Matias Quintana, Clayton Miller
Thus, the study proposes a conditional diffusion model for generating high-quality synthetic energy data using relevant metadata.
1 code implementation • 28 Nov 2023 • Antonio Liguori, Matias Quintana, Chun Fu, Clayton Miller, Jérôme Frisch, Christoph van Treeck
While no significant improvement is observed in terms of reconstruction error with the proposed PI-DAE, its enhanced robustness to varying rates of missing data and the valuable insights derived from the physics-based coefficients create opportunities for wider applications within building systems and the built environment.
1 code implementation • 12 Jul 2023 • Chun Fu, Matias Quintana, Zoltan Nagy, Clayton Miller
Another challenge is the lack of application of state-of-the-art imputation methods for missing gaps in energy data.
1 code implementation • 5 Aug 2022 • Matias Quintana, Stefano Schiavon, Federico Tartarini, Joyce Kim, Clayton Miller
On the other hand, for half and one third of each dataset occupant population, using Cohort Comfort Models, with less historical data from target occupants, Cohort Comfort Models increased their thermal preference prediction by 8~\% and 5~\% on average, and up to 36~\% and 46~\% for some occupants, when compared to general-purpose models trained on the whole population of occupants.
1 code implementation • 13 Mar 2022 • Matias Quintana, Till Stoeckmann, June Young Park, Marian Turowski, Veit Hagenmeyer, Clayton Miller
Data-driven building energy prediction is an integral part of the process for measurement and verification, building benchmarking, and building-to-grid interaction.
1 code implementation • 28 Sep 2020 • Matias Quintana, Stefano Schiavon, Kwok Wai Tham, Clayton Miller
However, when classes representing discomfort are merged and reduced to three, better imbalanced performance is expected, and the additional increase in performance by $\texttt{comfortGAN}$ shrinks to 1-2%.
2 code implementations • 4 Jul 2020 • Prageeth Jayathissa, Matias Quintana, Mahmoud Abdelrahman, Clayton Miller
These classification models were trained on a feature set that was developed from time-series attributes, environmental and near-body sensors, heart rate, and the historical preferences of both the individual and the comfort group assigned.
Human-Computer Interaction Applications