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Found 7 papers, 7 papers with code
Creating synthetic energy meter data using conditional diffusion and building metadata
Thus, the study proposes a conditional diffusion model for generating high-quality synthetic energy data using relevant metadata.
Opening the Black Box: Towards inherently interpretable energy data imputation models using building physics insight
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.
Filling time-series gaps using image techniques: Multidimensional context autoencoder approach for building energy data imputation
Another challenge is the lack of application of state-of-the-art imputation methods for missing gaps in energy data.
Cohort comfort models -- Using occupants' similarity to predict personal thermal preference with less data
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.
ALDI++: Automatic and parameter-less discord and outlier detection for building energy load profiles
Data-driven building energy prediction is an integral part of the process for measurement and verification, building benchmarking, and building-to-grid interaction.
Balancing thermal comfort datasets: We GAN, but should we?
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%.
Humans-as-a-sensor for buildings: Intensive longitudinal indoor comfort models
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
