Exterior lighting computer-automated design based on multi-criteria parallel evolutionary algorithm: optimized designs for illumination quality and energy efficiency

被引:16
作者
Rocha, Hugo [1 ,2 ]
Peretta, Igor S. [2 ]
Lima, Gerson Flavio M. [2 ]
Marques, Leonardo G. [2 ]
Yamanaka, Keiji [2 ]
机构
[1] Fed Inst Goias, BR-75524010 Itumbiara, Go, Brazil
[2] Univ Fed Uberlandia, Fac Elect Engn, BR-38400902 Uberlandia, MG, Brazil
关键词
Computer-automated exterior lighting design; Illumination quality; Energy efficiency; Parallel evolutionary algorithm; Multi-objective optimization; NSGA-II; MULTIOBJECTIVE OPTIMIZATION; GENETIC ALGORITHM;
D O I
10.1016/j.eswa.2015.09.046
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
A proper professional lighting design implies in a continuous search for the best compromise between both low power consumption and better lighting quality. This search converts this design into a hard to solve multi-objective optimization problem. Evolutionary algorithms are widely used to attack that type of hard optimization problems. However, professionals could not benefit from that kind of assistance since evolutionary algorithms have been unexplored by several commercial lighting design computer-aided softwares. This work proposes a system based on evolutionary algorithms which implement a computer-automated exterior lighting design both adequate to irregular shaped areas and able to respect lighting pole positioning constraints. The desired lighting design is constructed using a cluster of computers supported by a web client, turning this application into an efficient and easy tool to reduce project cycles, increase quality of results and decrease calculation times. This ELCAutoD-EA system consists in a proposal for a parallel multi-objective evolutionary algorithm to be executed in a cluster of computers with a Java remote client. User must choose lighting pole heights, allowed lamps and fixtures, as well as the simplified blue print of the area to be illuminated, marking the sub-areas with restrictions to pole positioning. The desired average illuminance must also be informed as well as the accepted tolerance. Based on user informed data, the developed application uses a dynamic representation of variable size as a chromosome and the cluster executes the evolutionary algorithm using the Island model paradigm. Achieved solutions comply with the illumination standards requirements and have a strong commitment to lighting quality and power consumption. In the present case study, the evolved design used 37.5% less power than the reference lighting design provided by a professional and at the same time ensured a 227.3% better global lighting uniformity. A better lighting quality is achieved because the proposed system solves multi-objective optimization problems by avoiding power wastes which are often unclear to a professional lighting engineer in charge of a given project. (C) 2015 Elsevier Ltd. All rights reserved.
引用
收藏
页码:208 / 222
页数:15
相关论文
共 29 条
  • [1] ABNT, 2012, 5101 ABNT NBR
  • [2] [Anonymous], LIGHT HDB REF APPL
  • [3] Constrained, mixed-integer and multi-objective optimisation of building designs by NSGA-II with fitness approximation
    Brownlee, Alexander E. I.
    Wright, Jonathan A.
    [J]. APPLIED SOFT COMPUTING, 2015, 33 : 114 - 126
  • [4] Multi-objective optimization of a nearly zero-energy building based on thermal and visual discomfort minimization using a non-dominated sorting genetic algorithm (NSGA-II)
    Carlucci, Salvatore
    Cattarin, Giulio
    Causone, Francesco
    Pagliano, Lorenzo
    [J]. ENERGY AND BUILDINGS, 2015, 104 : 378 - 394
  • [5] Energy-saving light positioning using heuristic search
    Castro, Francesc
    del Acebo, Esteve
    Sbert, Mateu
    [J]. ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE, 2012, 25 (03) : 566 - 582
  • [6] CELG, 2010, ANN ADM REP 2010
  • [7] Collet P., 2013, NATURAL COMPUTING SE, P35, DOI DOI 10.1007/978-3-642-37959-8_3
  • [8] A fast and elitist multiobjective genetic algorithm: NSGA-II
    Deb, K
    Pratap, A
    Agarwal, S
    Meyarivan, T
    [J]. IEEE TRANSACTIONS ON EVOLUTIONARY COMPUTATION, 2002, 6 (02) : 182 - 197
  • [9] European Commission, 2013, EN STRAT EUR
  • [10] Goldberg D.E., 1989, Optimization, and machine learning