What question did this study set out to answer?

The aim is to develop models predicting daily indoor temperature and relative humidity using a comprehensive dataset.

February 22, 2026Building and Environment0 citationsOpen Access

Prediction of Daily Home Indoor Temperature and Relative Humidity Using a Deep Ensemble Machine Learning Approach

YZYu ZhaoUniversitat Pompeu Fabra TGToni GalmésBarcelona Biomedical Research Park KSKarl SamuelssonUniversity of Gävle

Key Points

The aim is to develop models predicting daily indoor temperature and relative humidity using a comprehensive dataset.
Utilized a large dataset of 978 participants across 1,029 homes
Employed a Deep Ensemble Machine Learning approach
Incorporated 56 predictors related to meteorology, building, and occupant factors
Conducted short-term and long-term model validations
Analyzed feature importance for predictive accuracy
Achieved excellent short-term model performance for temperature (R² = 0.978) and humidity (R² = 0.894)
Showed good long-term performance for indoor temperature (R² = 0.891) and moderate performance for humidity (R² = 0.499)
Identified previous day's outdoor temperature and same-day outdoor humidity as key predictors

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.

AIに質問

Bookmark

View Full Paper