The increase of automation in buildings in order to diminish electric lighting and increase daylight exposure for health, comfort and energy saving purposes suffers from negative user perception. The project aims at studying the potential of a purposeful interplay between a user-centric interface design and an intelligent daylight and electric lighting control, in order to optimize comfort, promote its user acceptance and, in definitive, save electric energy.

The project, led by Dr. Julien Nembrini and Dr Jérôme Kaempf started in March 2020, is conducted by the Human-IST Institute in collaboration with Idiap Energy Informatics Group and Regent Lighting.

The final results of this project included producing a prototype system tested through users’ experiments (1) in a controlled environment, (2) in existing office settings in the premises of Smart Living Lab (Fribourg) and Idiap (Martigny). This prototype put in interplay a task light with the building’s general lighting and blinds to produce an energy optimized illuminance level, without generating glare for the user. This optimization is achieved through a surrogate model based on Machine Learning (ML) trained from detailed ray-tracing analysis simulations. The system architecture developed is highly modular and has demonstrated its adaptability to the different study conditions, in particular controlling only desktop/general lighting at the Smart Living Lab, while controlling desktop/ general lighting and blinds at Idiap. Results of testing the system with 60 user in a controlled environment demonstrated users’ comfort without difference of productivity between automated or manual condition, although the system used significantly less energy in the automated condition. Results from longitudinal studies confirm users’ satisfaction with similar performance gains despite the prolonged usage of the system.

Project description

The project is supported by the Swiss Federal Office of Energy (SFoE) within the LUCIDELES project grant. The content and conclusions from the project only engage the participants' responsibility and not SFoE.

Main contributors

ofenhuman-istidiapregent

People

Main applicants:

  • Julien Nembrini (Human-IST)
  • Jérôme Kaempf (Idiap)

Other applicants:

  • Kornelius Reutter (Regent Lighting)

Collaborators:

  • Moreno Colombo (Human-IST)
  • Michael Papinutto (Human-IST)
  • Roberto Boghetti (Idiap)
  • Chantal Basurto (Idiap)

Publications

  • An exploratory interplay between daylight, general and task lighting for visual comfort and electricity savings in a personal office space, Chantal Basurto, Michael Papinutto, Moreno Colombo, Roberto Boghetti, Kornelius Reutter, Julien Nembrini, Jérôme Kämpf, Eurosun2022 Conference, 25-29 Septiembre 2022, Kassel Germany
  • Integrating daylight with general and task lighting: A longitudinal in-the-wild study in individual and open space working areas, Chantal Basurto, Michael Papinutto, Moreno Colombo, Roberto Boghetti, Kornelius Reutter, Julien Nembrini, Jérôme Kämpf, Solar Energy Advances 2 (2022). https://doi.org/10.1016/j.seja.2022.100027
  • Papinutto, M., Boghetti, R., Colombo, M., Basurto, C., Reutter, K., Lalanne, D., Kämpf, J. H., & Nembrini, J. (2022). Saving energy by maximising daylight and minimising the impact on occupants: An automatic lighting system approach. Energy and Buildings, 268, 112176. https://doi.org/10.1016/j.enbuild.2022.112176
  • Towards the integration of personal task-lighting in an optimised balance between electric lighting and daylighting: A user-centred study of emotion, visual comfort, interaction and form-factor of task lights., Papinutto, M., Colombo, M., Golsouzidou, M., Reutter, K., Lalanne, D., & Nembrini, J. (2021). Journal of Physics: Conference Series, 2042(1), 012115. https://doi.org/10.1088/1742-6596/2042/1/012115
  • Machine learning techniques for the daylight and electric lighting performance predictions, Chantal Basurto, Oliver Paul and Jérôme Kämpf, in: Proceedings of the 17th IBPSA Conference, , 1-3 September, Bruges, Belgium, 2021. https://doi.org/10.268668/25222708.2021.30387
  • Implementation of machine learning techniques for the quasi real-time blind and electric lighting optimization in a controlled experimental facility, Chantal Basurto, Roberto Boghetti, Moreno Colombo, Michael Pappinutto, Julien Nembrini and Jérôme Kämpf, in: Journal of Physics: Conference Series, IOP Publishing, 2021. doi:10.1088/1742-6596/2042/1/012112

Prior related research whose results/methodologies are used in the project has been published by the project partners in:

  • Papinutto, M., Nembrini, J., & Lalanne, D. (2020). “Working in the dark?” investigation of physiological and psychological indices and prediction of back-lit screen users’ reactions to light dimming. Building and Environment, 186, 107356. open access link
  • Basurto, C., Kämpf H. Jérôme (2020). “An Integrated and strategic evaluation of automatic blind controls to achieve energy and occupant’s comfort objectives”. Proceedings of the Building Simulation and Optimization Conference BSO-V-2020, September 21st and 22st, Loughborough University, UK.