Opening the Black Box: Towards inherently interpretable energy data imputation models using building physics insight

arXiv:2311.16632v2 Announce Type: replace-cross
Abstract: Missing data are frequently observed by practitioners and researchers in the building energy modeling community. In this regard, advanced data-driven solutions, such as Deep Learning methods, are typically required to reflect the non-linear behavior of these anomalies. As an ongoing research question related to Deep Learning, a model's applicability to limited data settings can be explored by introducing prior knowledge in the network. This same strategy can also lead to more interpretable predictions, hence facilitating …

abstract advanced arxiv behavior black box box building community cs.lg data data-driven deep learning energy imputation insight linear modeling non-linear physics regard researchers solutions stat.ml type