Accurate and efficient adjustment of maintained illuminance and illuminance uniformity in indoor environments with daylight variations is a tremendous challenge, mainly due to the nonlinear and time-variant nature of lighting control systems. In this paper, we propose a smart lighting control method for indoor environments with both dimmable (controllable) and uncontrollable external light sources. Targeting an indoor environment with multiple zones, each requiring a different lighting condition and equipped with an unequal number of photodetectors and dimmable light sources, this paper presents a novel control mechanism that determines the output flux of each luminary in such a way that each zone (1) receives the required maintained illuminance, (2) illuminance uniformity conditions are met inside each zone, and (3) the power consumption is optimized. This method uses a neural network to learn the impact of each luminary on the maintained illuminance of each zone and adjust the dimming level of the luminaries to establish the required illuminance in the zones. We also rely on photodetectors to measure the daylight illuminance continuously and use it as the bias value for the neural network. The new priority value allows losing some illuminance accuracy (by allowing lager difference between the actual and required maintained illuminance values) for low-priority zones to reduce power consumption. The method has been evaluated in different test cases by chaining the widely-used DIALux tool and some MATLAB toolboxes. The evaluation results show that the method can achieve considerable accuracy by yielding an average Mean Square Error of 1.2 between the demanded and sensed illuminance values. Furthermore, when all sensors except one reference sensor are removed from each zone (to increase user comfort or reduce cost), the mean square error is less than 25.4 across all considered test cases.

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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%.

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Accurate and efficient adjustment of luminaire's dimming level in a smart environment can be a huge challenge. Indoor lighting system as a nonlinear and time variant block, which consumes significant amount of electrical power is evaluated in this paper. In doing so, a control method is proposed to efficiently adjust luminaire's dimming level in a smart environment and to optimize energy and user's comfort level. The proposed control method takes advantages from neural network and its learning capabilities. In this research, photodetectors are placed at the work zones, where work zones can have different number of photodetectors without any increase in complexity and any adverse effect on the control system. The method is capable of adopting itself to daylight variations with high accuracy. A state machine is developed to implement the method. The method is implemented in MATLAB and lighting conditions are extracted in DIALux. Luminaire's dimming levels are determined with accuracy higher than 99%. Daylight is considered as a bias to the system and thus the network does not need to be trained by any variations. In a dynamic condition, when taking into account the variation in daylight, the system mean error does not exceed 3%.