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 [BibTeX] [Marc21]
Implementation of machine learning techniques for the quasi real-time blind and electric lighting optimization in a controlled experimental facility
Type of publication: Conference paper
Citation: Basurto_CISBAT2021
Publication status: Accepted
Booktitle: Journal of Physics: Conference Series
Series: 2042
Volume: 2021
Number: 012112
Year: 2021
Month: September
Publisher: IOP Publishing
URL: https://iopscience.iop.org/iss...
DOI: doi:10.1088/1742-6596/2042/1/012112
Abstract: Machine Learning techniques have been recently investigated as an alternative to the use of physical simulations, aiming to improve the response time of daylight and electric lighting performance-predictions. In this study, daylight and electric lighting predictor models are derived from daylighting RADIANCE simulations, aiming to provide visual comfort to office room occupants, with a reduced use of electric lighting. The aim is to integrate an intelligent control scheme, that, implemented on a small embedded 32-bit computer (Raspberry Pi), interfaces a KNX system for a quasi-real-time optimization of the building parameters. The present research constitutes a step towards the broader goal of achieving a unified approach, in which the daylight and electric lighting predictor models would be integrated in a Model Predictive Control. A verification of the ML performance is carried-out by comparing the model predictions to data obtained in monitoring sessions in autumn, winter and spring 2020-2021, resulting in an average MAPE of 19.3%.
Keywords: daylighting and electric lighting, Integrated Lighting Controls, machine learning, surrogate models, Visual comfort
Projects Idiap
Authors Basurto, Chantal
Boghetti, Roberto
Colombo, Moreno
Papinutto, Michael
Nembrini, Julien
Kämpf, Jérôme
Added by: [UNK]
Total mark: 0
  • Kampf_CISBAT2021_2021.pdf