An Artificial Neural Network for Analyzing Overall Uniformity in Outdoor Lighting Systems

Antonio Del Corte-Valiente, José Luis Castillo-Sequera, Ana Castillo-Martinez, José Manuel Gómez-Pulido and Jose-Maria Gutierrez-Martinez
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Antonio Del Corte-Valiente: Department of Computer Engineering, Polytechnic School, University of Alcala, 28871 Alcalá de Henares, Spain
José Luis Castillo-Sequera: Department of Computer Sciences, Polytechnic School, University of Alcala, 28871 Alcalá de Henares, Spain
Ana Castillo-Martinez: Department of Computer Sciences, Polytechnic School, University of Alcala, 28871 Alcalá de Henares, Spain
José Manuel Gómez-Pulido: Department of Computer Sciences, Polytechnic School, University of Alcala, 28871 Alcalá de Henares, Spain
Jose-Maria Gutierrez-Martinez: Department of Computer Sciences, Polytechnic School, University of Alcala, 28871 Alcalá de Henares, Spain

Energies, 2017, vol. 10, issue 2, 1-18

Abstract: Street lighting installations are an essential service for modern life due to their capability of creating a welcoming feeling at nighttime. Nevertheless, several studies have highlighted that it is possible to improve the quality of the light significantly improving the uniformity of the illuminance. The main difficulty arises when trying to improve some of the installation’s characteristics based only on statistical analysis of the light distribution. This paper presents a new algorithm that is able to obtain the overall illuminance uniformity in order to improve this sort of installations. To develop this algorithm it was necessary to perform a detailed study of all the elements which are part of street lighting installations. Because classification is one of the most important tasks in the application areas of artificial neural networks, we compared the performances of six types of training algorithms in a feed forward neural network for analyzing the overall uniformity in outdoor lighting systems. We found that the best algorithm that minimizes the error is “Levenberg-Marquardt back-propagation”, which approximates the desired output of the training pattern. By means of this kind of algorithm, it is possible to help to lighting professionals optimize the quality of street lighting installations.

Keywords: artificial neural networks; energy efficiency; lighting systems; lighting optimization; uniformity (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2017
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (2)

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