An ANN-based model for the prediction of internal lighting conditions and user actions in non-residential buildings

This paper presents an Artificial Neural Network (ANN) based approach able to predict the internal lighting conditions in a working environment, taking into account the daylight entering the respective space as well as the special requirements of each user. The model training procedure is based both on real illuminance and occupancy data (measurements throughout a year) and on simulations, in order to integrate all possible conditions. User preferences in respect to lighting and blinds are expressed through probability curves. Illuminance due to the external daylight is calculated and predicted throughout the whole year, depending on the weather conditions, the time of the day, the location and the office orientation. The work plane distance from the window and the usage of blinds are also considered. The proposed model is further implemented for the prediction and evaluation of energy consumption for lighting in a working space based on the user preferences.