Collection of real-life stories on occupant-building interaction
datasetposted on 2021-08-02, 08:22 authored by Lucile Julia Sarran, Connor Brackley
This dataset was collected as part of an industrial PhD project between Saint-Gobain Nordic A/S and the Technical University of Denmark conducted by Lucile Sarran, under the supervision of Carsten Rode, Christian Hviid, Jakub Kolarik and Elisabeth Wærsted.
This work was carried out in the context of the International Energy Agency's EBC Annex 79: Occupant-Centric Building Design and Operation: https://annex79.iea-ebc.org/. It is the result of the cross-subtask activity C.7 co-led by Lucile Sarran (DTU, firstname.lastname@example.org) and Connor Brackley (Concordia University, email@example.com). Please contact Connor Brackley for more information or improvement suggestions.
Please cite as: Sarran, Lucile; Brackley, Connor (2021): Collection of real-life stories on occupant-building interaction. Dataset. https://doi.org/10.11583/DTU.14706066. This work is licensed under CC-BY version 4.0.
This activity focused on identifying human-related drivers of occupant dissatisfaction in buildings, by asking researchers around the world to describe unexpected occupant behaviors observed in their research projects. Therefore a series of prompts was developed with the aim to stimulate storytelling among researchers. A board containing these prompts was created on Miro, an online platform based on sticky notes. In a first phase, the link to the board was distributed to Annex 79 participants:18 stories were collected and included in a pilot study.
The prompts were:
· “My story takes place in...”: building-related information (type, number of buildings, location)
· “The technology or building system...”: e.g., ventilation, smart thermostats, new controls
· “Was this an existing system or did you implement it?”
· “The goal with the technology/building system was to...” original aim of the technology
· “In real conditions, what happened was...”- the unexpected human-building interaction
· “Because...” – researcher’s interpretation of the events
· “What is this interpretation based on?...” - e.g. sensor data, observations, personal experience
· “What we can learn from this story, and what should be done...” - lessons learned and best practice suggestions.
Two documents have been uploaded: (1) a high-resolution picture (in PDF format) of the data collection tool, together with the 18 research stories included in the pilot study; (2) a CSV file containing the data from the 18 stories.
An article presenting the methodology and the results of this story collection was submitted to the IAQ 2020 conference, postponed to 2022.