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Application of a Differential Evolution Algorithm in the Design of Public Lighting Installations Maximizing Energy Efficiency

This article presents a differential evolution (DE) algorithm that can be used to plan public lighting for streets, roadways, and freeways and maximize the energy efficiency of the installation. The algorithm was applied to a model based on new relationships between the energy efficiency of street lighting systems and geometric parameters such as street width, luminaire height, and distance between poles. These relationships were derived from the regression analysis of a large sample of outputs. The results of this algorithm consisted of the luminaire arrangement (one-sided, two-sided staggered, and two-sided coupled), luminaire height, luminaire type, and pole spacing for the most energy-efficient installation. The input of the algorithm was the lighting class or illuminance level, street width, as well as various other luminaire parameters. When these results were compared with those of DIALux, the performance of this new method was found to be extremely satisfactory. Furthermore, the constraints applied guaranteed compliance with the recommendations of the International Commission on Illumination.
- University of Granada Spain
Optimization, Public lighting, Energy efficiency, Differential evolution algorithm
Optimization, Public lighting, Energy efficiency, Differential evolution algorithm
citations This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).12 popularity This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.Top 10% influence This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).Average impulse This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.Top 10%
