Prediction of daily home indoor temperature and relative humidity using a deep ensemble machine learning approach
Available modelling frameworks for estimating indoor temperature (T) and relative humidity (RH) for epidemiological studies remain scarce. We developed a modelling framework to assess the daily mean indoor T and RH. We monitored indoor T and RH at 1,029 homes of 978 participants from the Barcelona L...
| Autores: | , , , , , , , , , , , |
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| Tipo de recurso: | artículo |
| Estado: | Versión publicada |
| Fecha de publicación: | 2026 |
| País: | España |
| Institución: | Consejo Superior de Investigaciones Científicas (CSIC) |
| Repositorio: | DIGITAL.CSIC. Repositorio Institucional del CSIC |
| OAI Identifier: | oai:digital.csic.es:10261/424380 |
| Acceso en línea: | http://hdl.handle.net/10261/424380 https://api.elsevier.com/content/abstract/scopus_id/105032164858 |
| Access Level: | acceso abierto |
| Palabra clave: | Thermal comfort Climate change Indoor apparent temperature Indoor heat Indoor thermal determinants http://metadata.un.org/sdg/3 http://metadata.un.org/sdg/9 http://metadata.un.org/sdg/13 http://metadata.un.org/sdg/12 http://metadata.un.org/sdg/11 http://metadata.un.org/sdg/7 Ensure healthy lives and promote well-being for all at all ages Ensure access to affordable, reliable, sustainable and modern energy for all Build resilient infrastructure, promote inclusive and sustainable industrialization and foster innovation Make cities and human settlements inclusive, safe, resilient and sustainable Ensure sustainable consumption and production patterns Take urgent action to combat climate change and its impacts |
| Sumario: | Available modelling frameworks for estimating indoor temperature (T) and relative humidity (RH) for epidemiological studies remain scarce. We developed a modelling framework to assess the daily mean indoor T and RH. We monitored indoor T and RH at 1,029 homes of 978 participants from the Barcelona Life Study Cohort (BiSC), Spain (2018-2021), for one week each during the first and third trimesters of pregnancy. We applied a Deep Ensemble Machine Learning (DEML) approach to predict the daily mean indoor T and RH throughout pregnancy, which integrated predictions from three base models: Random Forest, eXtreme Gradient Boosting, and Gradient Boosting Machine. The models incorporated a comprehensive set of 56 predictor variables, including meteorological conditions, building and neighborhood characteristics, and occupants’ sociodemographic and behavioral characteristics. We applied a long-term validation to assess model performance across pregnancy and a short-term validation to evaluate daily fluctuation capture. The DEML model achieved excellent performance in the short-term validation (T: R² = 0.978, MAD = 0.312°C; RH: R² = 0.894, MAD = 1.666%), with a good performance for indoor T (R² = 0.891, MAD = 0.717°C) and a moderate performance for RH (R² = 0.499, MAD = 3.591%) in the long-term validation. Feature importance analysis indicated that the previous one-day mean outdoor T and the same-day outdoor RH were the most influential predictors for indoor T and RH, respectively. The model reliably predicted indoor T and RH, highlighting its utility for future epidemiological studies on health impacts of indoor exposure. |
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