CONF Basurto_CISBAT2021/IDIAP Implementation of machine learning techniques for the quasi real-time blind and electric lighting optimization in a controlled experimental facility Basurto, Chantal Boghetti, Roberto Colombo, Moreno Papinutto, Michael Nembrini, Julien Kämpf, Jérôme daylighting and electric lighting Integrated Lighting Controls machine learning surrogate models Visual comfort EXTERNAL http://publications.idiap.ch/attachments/papers/2021/Kampf_CISBAT2021_2021.pdf PUBLIC Journal of Physics: Conference Series 2042 2021 012112 2021 IOP Publishing https://iopscience.iop.org/issue/1742-6596/2042/1 URL doi:10.1088/1742-6596/2042/1/012112 doi 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%.