Simulation and Comparison of Controller Strategy for Artificial Lighting for Indoor Lighting System

This paper showcases comparison of the methods for dealing with artificial lighting control strategy aimed at optimizing desired illumination levels for an automated indoor lighting control system based on daylight availability, Indoor Lighting system which performs illumination control by dimming LED luminaries based on daylight luminance through the sensor. Here, system is designed to ensure a minimum recommended light level that is simulated in python environment. In this simulation, recommended lighting level for office space i.e, 500 Lux has been implemented. This system senses the brightness of different environments and automatically adjusts the brightness of the bulb to a level as per the requirement of the user. Here, three machine learning algorithms have been used for controlling the system: fuzzy logic controllers, ant-colony optimization, and ant-lion optimization. All three algorithms have been compared based on their performance results with respect to desired lux and net room lux. The adoption parameters are an LED module wherein a light sensor will be used for the simulation of a lighting system.