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Prediction of daily home indoor temperature and relative humidity using a deep ensemble machine learning approach.

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Title: Prediction of daily home indoor temperature and relative humidity using a deep ensemble machine learning approach.
Authors: Zhao, Yu1,2,3 (AUTHOR), Domínguez, Alan1,2,3 (AUTHOR), Samuelsson, Karl1,4 (AUTHOR), Galmes, Toni1 (AUTHOR), Ballester, Joan1 (AUTHOR), Peyrusse, Fabien1 (AUTHOR), Basagaña, Xavier1,2,3 (AUTHOR), Foraster, Maria5 (AUTHOR), Schwartz, Joel6 (AUTHOR), Sunyer, Jordi1,2,3,7 (AUTHOR), Rivas, Ioar1,3,8 (AUTHOR), Dadvand, Payam1,2,3 (AUTHOR) payam.dadvand@isglobal.org
Source: Building & Environment. May2026, Vol. 295, pN.PAG-N.PAG. 1p.
Subject Terms: *Humidity, *Epidemiological research, *Environmental health, *Weather, Ensemble learning, Architectural engineering, Human behavior
Geographic Terms: Spain
Abstract: • We developed indoor temperature and humidity models using ensemble machine learning. • The study used a large dataset from 978 participants across 1,029 homes. • Models included 56 predictors covering meteorology, building, and occupant factors. • Models captured daily fluctuations well and showed adequate long-term performance. • Models are applicable to future heat-related epidemiological studies. 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. [Display omitted] [ABSTRACT FROM AUTHOR]
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Database: GreenFILE
Description
Abstract:• We developed indoor temperature and humidity models using ensemble machine learning. • The study used a large dataset from 978 participants across 1,029 homes. • Models included 56 predictors covering meteorology, building, and occupant factors. • Models captured daily fluctuations well and showed adequate long-term performance. • Models are applicable to future heat-related epidemiological studies. 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. [Display omitted] [ABSTRACT FROM AUTHOR]
ISSN:03601323
DOI:10.1016/j.buildenv.2026.114392