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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]
Copyright of Building & Environment is the property of Pergamon Press - An Imprint of Elsevier Science and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
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  Data: Prediction of daily home indoor temperature and relative humidity using a deep ensemble machine learning approach.
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  Data: • 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]
– Name: AbstractSuppliedCopyright
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  Data: <i>Copyright of Building & Environment is the property of Pergamon Press - An Imprint of Elsevier Science and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.)
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      – Type: doi
        Value: 10.1016/j.buildenv.2026.114392
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      – Code: eng
        Text: English
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      Pagination:
        PageCount: 1
        StartPage: N.PAG
    Subjects:
      – SubjectFull: Humidity
        Type: general
      – SubjectFull: Epidemiological research
        Type: general
      – SubjectFull: Environmental health
        Type: general
      – SubjectFull: Weather
        Type: general
      – SubjectFull: Ensemble learning
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      – SubjectFull: Architectural engineering
        Type: general
      – SubjectFull: Human behavior
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      – SubjectFull: Spain
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      – TitleFull: Prediction of daily home indoor temperature and relative humidity using a deep ensemble machine learning approach.
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              M: 05
              Text: May2026
              Type: published
              Y: 2026
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