Comfort-as-a-Service: Designing a User-Oriented Thermal Comfort Artifact for Office Buildings

TL;DR

This paper introduces a user-centric approach to thermal comfort in office buildings, leveraging IoT data and machine learning to personalize comfort zones and improve employee well-being.

Contribution

It presents a novel framework integrating user feedback, IoT data, and machine learning to optimize individual thermal comfort in open office environments.

Findings

Achieved an average model R^2 of 41.5% in predicting comfort zones.

Developed a mechanism for real-time user feedback collection.

Enabled employees to make informed decisions on comfort and workspace choices.

Abstract

Most people spend up to 90 % of their time indoors. However, literature in the field of facility management and related disciplines mostly focus on energy and cost saving aspects of buildings. Especially in the area of commercial buildings, only few articles take a user-centric perspective and none of them considers the subjectivity of thermal comfort. This work addresses this research gap and aims to optimize individual environmental comfort in open office environments, taking advantage of changes in modern office infrastructure and considering actual user feedback without interfering with existing systems. Based on a Design Science Research approach, we first perform a user experience testing in an exemplary corporate office building. Furthermore, we build a mechanism to gather user feedback on environmental comfort. Based on this, we build a machine learning model including different IoT data sources (e.g. building data and weather data) with an average coefficient of determination of 41.5%. Using these insights, we are able to suggest current individual comfort zones within the building and help employees to make better informed decisions on where to sit or what to wear, to feel comfortable and work productively. Therefore, we contribute to the body of knowledge by proposing a user-centric design within a cross-disciplinary context on the basis of analytical processes.