Dataset of LSTM-Based Metamodels for Inverse Thermal Characterization of Façade Elements in Testing Facilities [* Cross-Reference *]

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Bre, Facundo; Boje, Calin; Kubicki, Sylvain, 2026, "Dataset of LSTM-Based Metamodels for Inverse Thermal Characterization of Façade Elements in Testing Facilities [* Cross-Reference *]", https://lore.list.lu/citation?persistentId=perma:LIST.HEDEHI, LIST Open Repository, V1
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Description
This dataset provides trained Long Short-Term Memory (LSTM) surrogate models, preprocessing artifacts, and example research datasets developed for the inverse thermal characterization of façade elements in testing facilities. The models reproduce transient façade thermal behavior originally simulated using EnergyPlus and enable short-window characterization of façade thermophysical properties under free-running conditions without the need for conductive heat-flux sensors. The dataset includes LSTM models for opaque and transparent façade elements, associated scaling and metadata files, example hourly façade datasets for 2023, and a demonstration script for prediction and visualization. It supports reproducible research in AI-based surrogate modeling, façade testing analysis, digital twin development, and building physics applications. (2026-03-16)
***This entry has been automatically imported via Datacite by LIST harvest scripts. Please refer to https://doi.org/10.5281/zenodo.18887185 for the original and latest version of the dataset and data downloads*** (2026-03-25)
Subject Computer and Information Science
Keyword LSTM, Surrogate modeling, Façade thermal characterization, EnergyPlus, digital twin, machine learning
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