%Aigaion2 BibTeX export from Idiap Publications
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@INPROCEEDINGS{Basurto_CISBAT2021,
                      author = {Basurto, Chantal and Boghetti, Roberto and Colombo, Moreno and Papinutto, Michael and Nembrini, Julien and K{\"{a}}mpf, J{\'{e}}r{\^{o}}me},
                    keywords = {daylighting and electric lighting, Integrated Lighting Controls, machine learning, surrogate models, Visual comfort},
                    projects = {Idiap},
                       month = sep,
                       title = {Implementation of machine learning techniques for the quasi real-time blind and electric lighting optimization in a controlled experimental facility},
                   booktitle = {Journal of Physics: Conference Series},
                      series = {2042},
                      volume = {2021},
                      number = {012112},
                        year = {2021},
                   publisher = {IOP Publishing},
                         url = {https://iopscience.iop.org/issue/1742-6596/2042/1},
                         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\%.},
                         pdf = {https://publications.idiap.ch/attachments/papers/2021/Kampf_CISBAT2021_2021.pdf}
}