AI-based modelling of individual user preferences for dynamic control of future-oriented LED lighting systems

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This research project deals with the development of an AI-based approach for the derivation of dynamically adaptive, user-oriented luminaire control curves on the basis of continuously recorded sensor input values for the realization of HCL-compatible system solutions for LED interior lighting. The term "control curve" summarizes all those parameters that can be varied more or less independently for each luminaire (these are e.g. illuminance, proportion of direct/indirect light, color temperature, proportion of indirect blue light, etc.). In order to determine optimal AI-based control curves, subjects are tested at different times of the day to determine their individual lighting preferences depending on both subjective-psychological and external influencing factors and to correlate these with the corresponding sensor data using machine-learning methods. The aim is to develop a lighting system which, based on the incoming sensor data and an underlying user preference model, is capable of predicting the optimal control curve for the current situation and dynamically adjusting the illumination of the room over the course of the day. To reach this goal, a first study will be conducted where individual control curves are determined by recording user preferences in relation to the current lighting situation in the room at different time points over several working weeks. These user preferences are then related to the simultaneously recorded sensor data by means of AI-based evaluation. From this data, the control curves determined in this way are grouped into individual characteristic clusters depending on the environmental conditions and the subject-specific influencing factors. In a further iteration step, a second study will be conducted where these clustered control curves are then presented to the participants for further adaptation to their individual preferences by providing various adjustment options. These additional input values, together with the continuously recorded sensor data, serve to further optimize the control curves. In the third part of the research project, a physiological evaluation of these user-preferred optimal control curves will be carried out in order to address the question of alerting effects and to finally derive a corresponding user preference model as a basis for future, intelligent lighting systems.