Integrating daylight with general and task lighting: A longitudinal in-the-wild study in individual and open space working areas
| Type of publication: | Journal paper |
| Citation: | Basurto_SOL.ENERGYADV._2022 |
| Publication status: | Published |
| Journal: | Solar Energy Advances |
| Volume: | 2 |
| Year: | 2022 |
| Month: | November |
| ISSN: | 2667-1131 |
| URL: | http://10.1016/j.seja.2022.100... |
| DOI: | https://doi.org/10.1016/j.seja.2022.100027 |
| Abstract: | A control strategy targeting energy and comfort goals, is implemented in an individual and an open-space office, to control blinds and dimming lighting as part of an MPC intelligent control system. The study aims to achieve occupant's com- fort and an improved energy use in a quasi-real time optimization of the interior environment, using discomfort glare and work plane illuminance as KPIs. By the inclusion of task lighting, the user's preferences in regards to the interior lighting environment (CCT and lighting intensity), are considered. Those are assessed by a longitudinal user experiment, along with its benefits for the electric lighting energy reduction. Aiming to more responsive predictions, daylight and electric lighting surrogate models (LightGBM) were developed for each room based on year-round RADIANCE simulations. Implemented on a 32-bit computer (Raspberry-Pi 3B), the intelligent control system would promptly adjust blinds and dimming lighting through a KNX system, based on the model's prediction of the KPIs. The results showed that the system manages to provide a comfortable interior environment for the occupants, while energy reductions of at least 49% could be achieved compared with a conventional building operation. While a rather uncommitted acceptance of the system was reported by the occupants, improvements can be achieved by allowing a higher level of control that could be customized according to personality traits. |
| Keywords: | Model Predictive Control (MPC)Blinds and electric lighting automated controlsDaylight and electric lighting predictive modelsControlled user experiment |
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Idiap |
| Authors | |
| Added by: | [UNK] |
| Total mark: | 0 |
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