Optimizing Public Indoor Lighting System with Daylight-Based Illumination Control

Abstract

This paper presents an optimization framework for public indoor lighting system using metaheuristic algorithms to determine the optimal lighting layout, select energy-efficient lamp combinations, and minimize energy consumption through daylight-based ON/OFF illumination control. The framework is applied to a multipurpose hall, aiming to ensure adequate lighting illuminance while reducing electricity usage and installation costs. The process begins with optimizing lamp layouts and positions using Evolutionary Programming (EP), Genetic Algorithm (GA), and Particle Swarm Optimization (PSO). The best-performing algorithm is then adopted as the base model for further optimization. Different lamp types—10 W and 12 W light-emitting diodes (LEDs), 25 W compact fluorescent lamps (CFLs), and 36 W fluorescent lamps—are optimally combined to achieve additional cost and energy savings. A daylight-responsive control system is incorporated, utilizing hourly daylight data to switch lamps ON or OFF when required. Results show that the mixed-lamp configuration lowers both power consumption and initial installation costs, while the daylight-based control significantly reduces overall energy consumption. The optimized system complies with Malaysia Standards 1525 (MS 1525) and supports energy efficiency and sustainability in public buildings.