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Found 4 papers, 2 papers with code
Using causal inference to avoid fallouts in data-driven parametric analysis: a case study in the architecture, engineering, and construction industry
The decision-making process in real-world implementations has been affected by a growing reliance on data-driven models.
Machine Learning for Smart and Energy-Efficient Buildings
In this work, we review the ways in which machine learning has been leveraged to make buildings smart and energy-efficient.
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.
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%.
