Abstract
Design of an indoor lighting system involves, in general, a number of design parameters, viz. maintained average illuminance, uniformity of illuminance, unified glare rating, lighting power density. These design parameters are mutually conflicting and at the same time constrained because recommended value of each of the design parameter is to be satisfied simultaneously by adjusting design variables which are also bounded by upper and lower limits. An indoor lighting design problem involves a small number of design variables, such as spacing between adjacent luminaires, mounting height of luminaires, and the objective function is non-differentiable and thus suitable to apply both the grid search optimization and particle swarm optimization methods. This study compares the performance of these two methods to find out optimized design solutions for a set of indoor lighting design strategies, usually come across by lighting designers. An objective function is formulated by assigning weights to the design parameters, where a specific design strategy can be selected by adjusting the weights. This study reveals that the performance of PSO is significantly better to meet the qualitative aspect of lighting ambiance; however, the improvement is not significant than the grid search method when a design strategy puts stress upon the quantitative aspect of lighting.
Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
