Illumination control of LED systems based on neural network model and energy optimization algorithm

Abstract

Lighting constitutes a large proportion of the main energy consumption loads of a building; energy-efficient lighting control is an important topic to be addressed in achieving green building requirement. Within a building, huge amount of lights are being deployed in a distributed manner which poses great challenge in achieving energy saving and personalized lighting control. In this paper, the objective is to satisfy table illumination preference of each office user while minimize energy consumption of the overall lighting system by optimizing the illumination levels of the distributed luminaires. A holistic and scalable neural network model is developed to represent the complex relationship between dimming levels of luminaires and measured illuminance on the table. Based on the developed model, a lighting energy optimization algorithm is proposed to achieve energy saving while having personalized lighting control. The proposed model can serve as a base model for the improved artificial light and even daylight control system in the future study.