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.

本文提出了一种基于人工神经网络（ANN）的方法，该方法可以预测工作环境中的内部照明条件，同时考虑到进入各自空间的日光以及每个用户的特殊要求。模型训练过程基于真实的照度和占用数据（一年中的测量值）以及模拟，以整合所有可能的条件。通过概率曲线表示用户对照明和百叶窗的偏好。全年根据天气情况，一天中的时间，位置和办公室的方位来计算和预测由于外部日光导致的照度。还考虑了工作平面到窗户的距离以及百叶窗的使用。