Dynamic Light and and Indoor Climate for Offices
Duration: 01/2018 – 12/2021 Funding: TKI Urban Energy 2017
Office buildings are generally climatized in a uniform fashion during working hours, which results in higher energy usage than necessary. A constant temperature is equated to a higher experience of comfort by users, but there is a large amount of individual variation in experienced comfort. Furthermore, comfort should not be seen as a synonym for health. Research by the University of Maastricht shows that exposure to mildly cold temperatures can significantly improve people’s health by activating brown adipose tissue and improving glucose levels. DYNKA adds the dimension of light to this equation as another factors which can influence health. Research has shown that varying light levels can positively affect the circadian rhythm, comfort, alertness, and general health of workers, while simultaneously lowering energy consumption.
The goal of DYNKA is to explore the degree to which varying the environmental light and temperature in office buildings can be used to have a beneficial effect on worker’s health and energy consumption, in actual installation concepts for innovative dynamic office systems. By leveraging technologies in this project, we expect that office environments will be conducive to higher productivity, as well as lowering energy costs by approximately 15%. This will be realised through the application of self-learning systems which can dynamically alter light and temperature based on the behavior and needs of workers.
Almende will develop a software platform that can dynamically control the light and temperature in an office building based on principles of self-learning and self-organization, and deploy said platform in an experimental pilot setting over the course of the project.
The project represents a bridge between the health and energy domains, both of which are highly relevant for Almende. We expect to be able to apply the insights and tools developed in the project in other energy-related projects, specifically when it comes to using self-learning systems to optimize energy usage over time.
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