No abstract

No abstract

Task-lighting is a well-known strategy to save energy by bringing light where it is most needed, providing adaptable localised light conditions of special interest in the current home-office context. Despite these benefits and in addition to negatively impacting biological rhythms, the generalization of backlit screens has made task lights less demanded, with screen users tending to accept significantly lower amounts of the illuminance standards. In parallel, the advantages of task-lighting may contradict the energy benefits of presence-driven lighting or blinds automation. This pilot experiment aims at evaluating the task light usage patterns and characteristic preferences for both paper and computer work from a user-centered perspective to provide guidelines in terms of luminaires characteristics. Thirteen participants evaluated three different task lights in both paper and computer conditions. Our results emphasize the role of the luminaire’s form factor, interface and lighting control characteristics, providing general recommendations on luminaire design.

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%.

No abstract