Human-shading interaction in shared offices: An experimental study
- 1School of Architecture & Built Environment, Queensland University of Technology, Brisbane 4000, Australia; Building 4.0 CRC, Melbourne 3145, Australia; Corresponding author.
- 2School of Architecture & Built Environment, Queensland University of Technology, Brisbane 4000, Australia
- 3School of Electrical Engineering & Robotics, Queensland University of Technology, Brisbane 4000, Australia
Abstract
The International Energy Agency (IEA) identifies occupant behavior, including human–shading interaction, as a major factor influencing building energy use. While most studies done today focus on single offices, shading behavior is particularly critical in shared offices, where one occupant’s adjustment may create discomfort for others. Instead of relying on mean comfort thresholds, advanced shading control systems require a deeper understanding of diverse behavioral patterns. This experimental study aims to examine human–shading interactions in shared offices using behavioral clustering analysis to classify occupants into distinct groups based on their shading operation patterns. The study involved 16 participants across four desks with varying daylight exposure and access to high-quality window views. Participants were divided into four groups of four, with each group spending four full days in the experimental room, resulting in a 16-day data collection period. Both objective data (researcher observation of shading interactions) and subjective feedback (daily questionnaires) were collected. Temporal patterns of shading use were analysed across morning, noon, and afternoon periods. Clustering analysis using machine learning techniques revealed three distinct user profiles: Passive, Adaptive, and Active. The majority of participants (70%) were classified as Passive users, showing minimal interaction with the shading system, while smaller groups adopted more proactive or desk-specific strategies. Cluster-specific variations in behaviour were influenced by time of day, glare exposure, and social considerations. These findings highlight the importance of personalized or adaptive shading control strategies in shared spaces and establish a framework for occupant-centric shading controls that balance comfort with energy performance.
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BibTeX
@article{valitabar2026humanshading,
title = {{Human-shading interaction in shared offices: An experimental study}},
author = {Mahdi Valitabar and Veronica Garcia Hansen and Maryam Haghighat},
journal = {Energy and Built Environment},
year = {2026},
month = oct,
doi = {10.1016/j.enbenv.2025.09.001},
url = {https://doi.org/10.1016/j.enbenv.2025.09.001},
note = {CC BY},
}
RIS
TY - JOUR
TI - Human-shading interaction in shared offices: An experimental study
AU - Mahdi Valitabar
AU - Veronica Garcia Hansen
AU - Maryam Haghighat
PY - 2026
DA - 2026/10/30
JO - Energy and Built Environment
DO - 10.1016/j.enbenv.2025.09.001
UR - https://doi.org/10.1016/j.enbenv.2025.09.001
AB - The International Energy Agency (IEA) identifies occupant behavior, including human–shading interaction, as a major factor influencing building energy use. While most studies done today focus on single offices, shading behavior is particularly critical in shared offices, where one occupant’s adjustment may create discomfort for others. Instead of relying on mean comfort thresholds, advanced shading control systems require a deeper understanding of diverse behavioral patterns. This experimental study aims to examine human–shading interactions in shared offices using behavioral clustering analysis to classify occupants into distinct groups based on their shading operation patterns. The study involved 16 participants across four desks with varying daylight exposure and access to high-quality window views. Participants were divided into four groups of four, with each group spending four full days in the experimental room, resulting in a 16-day data collection period. Both objective data (researcher observation of shading interactions) and subjective feedback (daily questionnaires) were collected. Temporal patterns of shading use were analysed across morning, noon, and afternoon periods. Clustering analysis using machine learning techniques revealed three distinct user profiles: Passive, Adaptive, and Active. The majority of participants (70%) were classified as Passive users, showing minimal interaction with the shading system, while smaller groups adopted more proactive or desk-specific strategies. Cluster-specific variations in behaviour were influenced by time of day, glare exposure, and social considerations. These findings highlight the importance of personalized or adaptive shading control strategies in shared spaces and establish a framework for occupant-centric shading controls that balance comfort with energy performance.
SN - 2666-1233
ER -