A machine-learning approach for evaluating occupants’ indoor environment satisfaction in high-rise mixed-use buildings
Date
2025
Authors
Croffi, J.
Soebarto, V.
Kroll, D.
Barrie, H.
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Journal article
Citation
Smart and Sustainable Built Environment, 2025; 1-30
Statement of Responsibility
Juliana Croffi, Veronica Soebarto and David Kroll, Helen Barrie
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Abstract
Purpose – This paper presents a pilot study of a machine learning (ML) approach to predict occupants’ satisfaction with the indoor environment in high-rise mixed-use buildings, aiming to validate a proof of concept for integrating ML models into early-stage design tools to support occupant-centred performance evaluation. Design/methodology/approach – Using post-occupancy evaluation data from a case study building, Random Forest and Neural Network models were trained to classify satisfaction levels–Dissatisfied, Neutral or Satisfied–for both residents and workers based on indoor environmental factors. The methodology focuses on addressing class imbalance through data resampling and cost-sensitive learning, with model performance assessed using class-specific metrics. Findings – Both models achieved high overall accuracy (cross-validation score >0.80), with notable improved performance in identifying minority classes after balancing methods were employed. While limited to a single case study, future data collection across diverse buildings and occupant profiles has the potential to improve performance and enable generalisability. Originality/value – This research demonstrates the feasibility of a scalable framework for predicting indoor environmental satisfaction, enabling the integration of ML models into simulation-based workflows for datadriven, occupant-centric design evaluation. It advances the field by (1) classifying satisfaction into three actionable categories while explicitly addressing class imbalance, (2) operationalising POE data to move beyond retrospective reporting and (3) establishing a proof of concept for embedding ML models into early-stage design tools.
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Published online: November 07 2025.
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© Juliana Croffi, Veronica Soebarto, David Kroll and Helen Barrie. Published by Emerald Publishing Limited. This article is published under the Creative Commons Attribution (CC BY 4.0) licence. Anyone may reproduce, distribute, translate and create derivative works of this article (for both commercial and non-commercial purposes), subject to full attribution to the original publication and authors. The full terms of this licence may be seen at Link to the terms of the CC BY 4.0 licence.