Prediction of the indoor climate in cultural heritage buildings through machine learning: first results from two field tests

Prediction of the indoor climate in cultural heritage buildings through machine learning: first results from two field tests

Abstract Control of temperature and relative humidity in storage areas and exhibitions is crucial for long-term preservation of cultural heritage objects. This paper explores the possibilities for developing a proactive system, based on a machine-learning model (XGBoost), for predicting the occurren...

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Bibliographic Details
Main Authors: Christian Boesgaard, Birgit Vinther Hansen, Ulla Bøgvad Kejser, Søren Højlund Mollerup, Morten Ryhl-Svendsen, Noah Torp-Smith
Format: Article
Language:English
Published: SpringerOpen 2022-10-01
Series:Heritage Science
Subjects:
Relative humidity
Indoor environment
Prediction
Climate forecast
Environmental control
Storage
Online Access:https://doi.org/10.1186/s40494-022-00805-3

Internet

https://doi.org/10.1186/s40494-022-00805-3

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