Zone Based Control Methodology of Smart Indoor Lighting Systems Using Feedforward Neural Networks

Abstract

A smart, accurate, and energy efficient control strategy to adjust dimming level of luminaires in an indoor environment is proposed in this paper. The control block in lighting system is nonlinear and time variant, since multiple reflections of objects and daylight variation are related to daytime and they can directly affect the system. According to the complexity of equations which model the lighting system, a control system based on Neural Network (NN) and learning machine is developed. By considering each zone as an independent structure, occupancy in each zone is added. In addition, photodetectors are placed at the work zones and hence increasing the accuracy. The occupancy condition for other zones in the environment are considered as bias to the inputs of the system. Therefore, multiple reflections in the environment are considered in the design of the proposed control method. Accuracy and system performance is improved by separation of control block for each zone as an autonomous control unit, whereas complexity of the system is reduced. The proposed design is evaluated in test beds developed using DIALux and MATLAB. The mean error varies according to the effect of zones on each other. The method is suitable for indoor environment that zones does not have common luminaires. The mean error in the case study that is not proper for the method does not exceed 20%. Although, the error seems to be high but compared to the methods that have ceiling mount sensors is accurate and power and power efficient. Besides, the case with zones that has separated luminaires the mean error is less than 5%.

Melanopic Limits of Metamer Spectral Optimisation in Multi-Channel Smart Lighting Systems

“…A disadvantage of using optimisation frameworks in a lighting controller, which evaluates additional sensor input data, is the processing time and the scalability for larger office lighting systems [84,88]. Recent works developed new approaches based on neural networks to replace classical optimisation frameworks in closed-loop lighting control units, appealing with scalability and reduced computation time [78,79,84,89]. Incorporating environmental sensors into a fully automated lighting system can minimise energy consumption at the expense of user acceptance, as individual lighting preferences are not considered.…”

Daylight adaptive smart indoor lighting control method using artificial neural networks

“…The faults in the system is alarmed to the user and object variation and movement in the environment necessitate re-training the ANN, as it may change the reflection pattern and thereby, the relation between luminaires and photodetectors are changed. Daylight variation, however, is measured by the photodetectors and is taken into the account by as a bias [14]. In order to control and deal with such variations, a bias is calculated and added to ANNs input.…”

“…The heart of a typical smart lighting system is a controller unit that adjusts the luminaires dimming level according to the photodetectors' output, occupancy condition, and type of visual tasks running in each zone. Design of efficient control algorithms and mechanisms for smart indoor lighting systems is complex task and have attracted considerable attention over recent years [14][15][16]. The complexity of such designs stems from the fact that lighting systems are by nature nonlinear and time-variant (NLTV).…”

The Energy-Efficient Control Solutions of Smart Street Lighting Systems: A Review, Issues, and Recommendations

“…A typical smart lighting system's brain is a control unit that modifies the luminaire's dimming level based on the output of the photodetectors, the presence or absence of occupants, and the types of visual activities being performed in each zone. The development of effective control mechanisms and algorithms for smart lighting systems is a challenging problem that has received a lot of attention recently [27][28] [29]. Andrzej Ożadowicz et al [30], reported a study on the examination and creation of lighting control algorithms.…”