Volume 8, Issue 6, November 2019, Page: 165-175
Color Influence and Genetic Algorithm Optimization in Interior Lighting Building
Merim´e Souffo Tagueu, Laboratoire de G´enie Electrique, M´catronique et Traitement du Signal, National Advanced School of Engineering, University of Yaound´e I, Yaound´e, Cameroon
Benoˆıt Ndzana, Laboratoire de G´enie Electrique, M´catronique et Traitement du Signal, National Advanced School of Engineering, University of Yaound´e I, Yaound´e, Cameroon
Received: Oct. 28, 2019;       Accepted: Nov. 20, 2019;       Published: Dec. 30, 2019
DOI: 10.11648/j.epes.20190806.14      View  38      Downloads  33
The energy consumed by the lighting of the buildings represents a not negligible part of the total energy. The use of low-energy luminaires such as LEDs has significantly reduced this consumption, in addition to the reduction of greenhouse gases and the extended life of the lamps. To satisfy the basic principles of optimal lighting system design (i.e., maximizing uniformity and reducing the level of illumination by staying within the required normative range), many researches using optimization algorithms have been conducted with interesting results. This article proposes a multi-objective optimization model integrating the influence of the colors (in particular primary colors), of the different compartments of a room on the level of total illumination of the piece. The reduction of energy consumption is demonstrated by considering a specific model of illumination in which we introduced the reflection factor related to the colors of the surrounding environment. The subsequent use of genetic algorithms (NSGA III) makes it possible to find the optimal coefficient of variation of the LEDs or any other variable luminaires to have the desired energy value while keeping the same comfort for the users. The proposed model is implemented for the case of an office room. The results show an energy savings of up to 39% with red color. Of particular, results are obtained while maintaining regular illumination and changing the color of the pieces.
Illumination, Multi-objective Optimization, Color, NSGA III, Energy Savings
To cite this article
Merim´e Souffo Tagueu, Benoˆıt Ndzana, Color Influence and Genetic Algorithm Optimization in Interior Lighting Building, American Journal of Electrical Power and Energy Systems. Vol. 8, No. 6, 2019, pp. 165-175. doi: 10.11648/j.epes.20190806.14
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K. Beddiar and J. Lemale. Bˆatiment Intelligent Et Efficacit´e Energetique Optimisation, nouvelles technologies et BIM. Dunod, 2016.
Consumption of energy, 2017. https://ec.europa.eu/eurostat/statisticsexplained/ index.php?title=Archive :Consumption of energy.
Jelena Popovi´c-Gerber, Jesus Angel Oliver, Nicol´as Cordero, Thomas Harder, Jos´e A Cobos, Michael Hayes, Sen Cian OMathuna, and Erich Prem. Power electronics enabling efficient energy usage : Energy savings potential and technological challenges. IEEE transactions on power electronics, 27 (5): 2338-2353, 2012.
V.-Kitio. Int´egration des mesures defficacit´e ´energ´etique et de conservation des ressources dans les normes de construction au cameroun, Juillet 2014. http://arpedac.org/wpcontent/uploads/2017/04/Projet-ONUHabitat-MiHDU-vincent-Kitio.pdf.
Marie-Claude Dubois and ?ke Blomsterberg. Energy saving potential and strategies for electric lighting in future north european, low energy office buildings : A literature review. Energy and buildings, 4 (10): 2572-2582, 2011.
Pierre-Ren´e Bauquis. A reappraisal of energy supply and demand in 2050. Oil and Gas Science and Technology, 56(4): 389-402, 2001.
Jae-Wook Lee, Hyung-Jo Jung, Ji-Young Park, JB Lee, and Yoonjin Yoon. Optimization of building window system in asian regions by analyzing solar heat gain and daylighting elements. Renewable energy, 50: 522-531, 2013.
G Baldinelli, F Asdrubali, C Baldassarri, F Bianchi, F DAlessandro, S Schiavoni, and C Basilicata. Energy and environmental performance optimization of a wooden window : A holistic approach. Energy and buildings, 79: 114-131, 2014.
Youssef Bichiou and Moncef Krarti. Optimization of envelope and hvac systems selection for residential buildings. Energy and Buildings, 43 (12): 3373-3382, 2011.
Gianluca Rapone and Onorio Saro. Optimisation of curtain wall facades for office buildings by means of psoalgorithm. Energy and Buildings, 45: 189-196, 2012.
Ramzi Ouarghi and Moncef Krarti. Building shape optimization using neural network and genetic algorithm approach. Ashrae transactions, 112 (1), 2006.
Daniel Tuhus-Dubrow and Moncef Krarti. Geneticalgorithm based approach to optimize building envelope design for residential buildings. Building and environment, 45 (7): 1574-1581, 2010. 10 (2), 181C192.
Eddy Prianto and Patrick Depecker. Optimization of architectural design elements in tropical humid region with thermal comfort approach. Energy and buildings, 35 (3): 273-280, 2003.
Francesco Asdrubali, Francesco DAlessandro, and Samuele Schiavoni. A review of unconventional sustainable building insulation materials. Sustainable Materials and Technologies, 4: 1-17, 2015.
Jiangjiang Wang, Zhiqiang John Zhai, Youyin Jing, and Chunfa Zhang. Particle swarm optimization for redundant building cooling heating and power system. Applied Energy, 87 (12): 3668-3679, 2010.
Ebrahim Solgi, Zahra Hamedani, Ruwan Fernando, Henry Skates, and Nnamdi Ezekiel Orji. A literature review of night ventilation strategies in buildings. Energy and Buildings, 2018.
Jens Pfafferott, Sebastian Herkel, and Matthias Wambsgan?. Design, monitoring and evaluation of a low energy office building with passive cooling by night ventilation. Energy and buildings, 36 (5): 455-465, 2004.
Jared Landsman. Performance, prediction and optimization of night ventilation across different climates. 2016.
Natasa Djuric, Vojislav Novakovic, Johnny Holst, and Zoran Mitrovic. Optimization of energy consumption in buildings with hydronic heating systems considering thermal comfort by use of computer-based tools. Energy and Buildings, 39 (4): 471-477, 2007.
AmarMKhudhair and MohammedMFarid. A review on energy conservation in building applications with thermal storage by latent heat using phase change materials. Energy conversion and management, 45 (2): 263-275, 2004.
Juan F De Paz, Javier Bajo, Sara Rodrłguez, Gabriel Villarrubia, and Juan M Corchado. Intelligent system for lighting control in smart cities. Information Sciences, 372: 241-255, 2016.
Nandha Kumar Kandasamy, Giridharan Karunagaran, Costas Spanos, King Jet Tseng, and Boon-Hee Soong. Smart lighting system using ann-imc for personalized lighting control and daylight harvesting. Building and Environment, 139: 170C180, 2018.
Wa Si, Harutoshi Ogai, Katsumi Hirai, Hidehiro Takahashi, and Masatoshi Ogawa. An improved pso method for energy saving system of office lighting. In SICE Annual Conference 2011, pages 1533-1536. IEEE, 2011.
M Corcione and L Fontana. Optimal design of outdoor lighting systems by genetic algorithms. Lighting Research & Technology, 35 (3): 261-277, 2003.
Francisco Chueco, Fernando Lpez, and Miguel Bobadilla. Technical and economic evaluation of fluorescent and led luminaires in underground mining. a case study : New mine level of el teniente. Energy and Buildings, 93: 16-22, 2015.
ZizhenWang and Yen Kheng Tan. Illumination control of led systems based on neural network model and energy optimization algorithm. Energy and Buildings, 62: 514-521, 2013.
Evangelos-Nikolaos D Madias, Panagiotis A Kontaxis, and Frangiskos V Topalis. Application of multi-objective genetic algorithms to interior lighting optimization. Energy and Buildings, 125: 66-74, 2016.
Souffo Tagueu Merim and Ndzana Beno?t. Lighting optimisation control of fluo/led systems using neural network and mathematical model. International Journal of Electrical Engineering & Technology, 10 (4): 47-59, 2019.
Lawrence D Woolf. Confusing color concepts clarified. The Physics Teacher, 37 (4): 204-206, 1999.
Light and lighting-lighting of work places ? part 1: Indoor work places, June 2011. European Committee for Standardization.
Yao-Jung Wen and AM Agogino. Control of wirelessnetworked lighting in open-plan offices. Lighting Research & Technology, 43 (2): 235-248, 2011.
Michael Fischer, KuiWu, and Pan Agathoklis. Intelligent illumination model-based lighting control. In 2012 32nd International Conference on Distributed Computing Systems Workshops, pages 245-249. IEEE, 2012.
Shigang Cui, Huimin Lv, Xingli Wu, Yongli Zhang, and Lin He. Optimization of plant light source based on simulated annealing particle swarm optimization algorithm. In 2018 Chinese Control And Decision Conference (CCDC), pages 700-703. IEEE, 2018.
Ralph Evins. A review of computational optimisation methods applied to sustainable building design. Renewable and sustainable energy reviews, 22: 230-245, 2013.
Shady Attia, Mohamed Hamdy, William OBrien, and Salvatore Carlucci. Assessing gaps and needs for integrating building performance optimization tools in net zero energy buildings design. Energy and Buildings, 60: 110-124, 2013.
Kalyanmoy Deb, Amrit Pratap, Sameer Agarwal, and TAMT Meyarivan. A fast and elitist multiobjective genetic algorithm: Nsga-ii. IEEE transactions on evolutionary computation, 6 (2): 182-197, 2002.
Hisao Ishibuchi, Ryo Imada, Yu Setoguchi, and Yusuke Nojima. Performance comparison of nsga-ii and nsga-iii on various many-objective test problems. In 2016 IEEE Congress on Evolutionary Computation (CEC), pages 3045-3052. IEEE, 2016.
Haitham Seada and Kalyanmoy Deb. U-nsga-iii : A unified evolutionary algorithm for single, multiple, and many-objective optimization. COIN report, 2014022, 2014.
Dali - digital addressable lighting interface. http://www.dali-ag.org.
Dialux documentation. https://www.dial.de/en/dialux/.
Kalyanmoy Deb and Himanshu Jain. An evolutionary many-objective optimization algorithm using referencepoint-based nondominated sorting approach, part i: solving problems with box constraints. IEEE Transactions on Evolutionary Computation, 18 (4): 577-601, 2014.
Matlab 2014 documentation. https://fr.mathworks.com/products/global-optimization.html.
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