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 | |
| Added by: | [UNK] |
| Total mark: | 0 |
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