Buildings are heavily responsible for energy consumption and CO2 emissions. Increasing their energy efficiency is in the direction of the EU strategies; and this is consistent with EU Energy Performance of Buildings Directive (EPBD 2010/31/EU), 2010. In this view the design and management of daylight and artificial light can play a fundamental role. In this paper, a method to optimize indoor lighting design from energy efficiency, economic and environmental viewpoints is presented. The optimization was developed through the Genetic Algorithms technique and the optimal layout in terms of number, location and mounting heights of luminaires in a generic office room was evaluated. Solutions with different types of light sources, nominal luminous flux and photometric distribution curves were compared and the cost effectiveness of these alternatives was also considered. The systems resulting from the optimization show increased uniformity of illuminance, a reduction of the number of luminaires and a decrease of the maximum UGR values, proving the effectiveness of the technique in the lighting design field. Results show that current methods for lighting design can still be improved to achieve optimal configurations in terms of energy efficiency and cost effectiveness.
Gugliermetti L, Galati C, Mattoni B, Nardecchia F and Bisegna F. Home smart grid device for energy saves and failure monitoring. In: Proceedings of the 15th international conference on environment and electrical engineering (EEEIC), Rome, Italy, June 10–13 2015.
4.
Halonen L, Tetri E and Bhusal P. Guidebook on energy efficient electric for buildings, [Annex 45]. Espoo, Finland: Aalto University School of Science and Technology 2010, http://www.ecbcs.org/docs/ECBCS_Annex_45_Guidebook.pdf (accessed 25 August 2015).
Royal Swedish Academy of Science. Efficient blue light-emitting diodes leading to bright and energy-saving white light sources [Scientific Background on the Nobel Prize in Physics 2014. Stockholm]. Sweden: Royal Swedish Academy of Science, 2014.
8.
MattoniBGugliermettiFBisegnaF. A multilevel method to assess and design the renovation and integration of Smart Cities. Sustainable Cities Soc2015; 15: 105–119.
9.
AghemoCBlasoLPellegrinoA. Building automation and control system: a case study to evaluate the energy and environmental performances of a lighting control system in offices. Automat Constr2014; 43: 10–22.
10.
Chourabi H, Taewoo N, Walker S, Gil-Garcia JR, Mellouli S, Nahon K, Pardo TA and Scholl HJ. Understanding Smart city: An integrative framework. In: Proceedings of the 45th Hawaii international conference on system science (HICSS), TBD Maui, HI, January 4–7 2012, pp.2289–2297.
11.
Dubois MC. A review of energy saving potential and strategies for electric lighting in future low energy office buildings. In: Rose L. Energy: modern energy storage, conversion, and transmission in the 21st century. Vancouver: Nova Science Publishers 2013, pp.47–78.
12.
GugliermettiFBisegnaF. Daylighting with external shading devices: design and simulation algorithms. Build Environ2006; 41: 136–149.
13.
Kanter RM and Litow SS. Informed and interconnected: a manifesto for smarter cities. Harvard Business School General Management Unit Working Paper 09-141, Boston, June 15 2009.
14.
Komninos N. The architecture of intelligent cities; integrating human, collective, and artificial intelligence to enhance knowledge and innovation. In: Proceedings of the 2nd IET international conference on intelligent environments, Athens, Greece, July 5–6 2006.
15.
Lo VersoVRMPellegrinoAPellereyF. A multivariate non-linear regression model to predict the energy demand for lighting in rooms with different architectural features and lighting control systems. Energ Build2014; 76: 151–163.
16.
BelliaLBisegnaF. From radiometry to circadian photometry: a theoretical approach. Build Environ2013; 62: 63–68.
17.
Berlov D, Pavlova L, Bisegna F, Aladov A, Baranova T, Chiligina J and Mizerov M. Prospects of research of influence of lighting modes on changes of human functional state with assistance of “smart lighting” device. In: Proceedings of the 15th international conference on environment and electrical engineering (EEEIC), Rome, Italy, June 10–13 2015.
18.
FerlazzoFPiccardiLBurattiniCBarbalaceMGianniniAMBisegnaF. Effects of new light sources on task switching and mental rotation performance. J Environ Psychol2014; 39: 92–100.
19.
GouZLauSS-YQianF. Comparison of mood and task performance in naturally-lit and artificially-lit environments. Indoor Built Environ2015; 24: 27–36.
20.
ŽukauskasAVaicekauskasRPranciškusV. Optimization of solid-state lamps for photobiologically friendly mesopic lighting. Appl Optics2012; 51: 8423–8432.
21.
CammaranoSPellegrinoALo VersoVRMAghemoC. Assessment of daylight in rooms with different architectural features. Build Res Inf2014; 43: 222–237.
22.
Begemann S, Aarts M and Tenner A. Daylight, artificial light and people. In: Proceedings of the 39th IES Lighting Convention, Sidney, Australia, November 15–17 1994.
23.
CantinFDuboisMC. Daylighting metrics based on illuminance, distribution, glare and directivity. Light Res Technol2011; 43: 291–307.
24.
DuboisMCFlodbergK. Daylight utilisation in perimeter office rooms at high latitudes: investigation by computer simulation. Light Res Technol2013; 45: 52–75.
25.
GugliermettiFBisegnaF. A model study of light control systems operating with Electrochromic Windows. Light Res Technol2005; 37: 3–20.
26.
HollandJH. Adaptation in natural and artificial systems, Ann Arbor, Michigan: University of Michigan Press, 1975.
27.
Conceição AntónioCAMonteiroJBAfonsoCF. Optimal topology of urban buildings for maximization of annual solar irradiation availability using a genetic algorithm. Appl Therm Eng2014; 73: 424–437.
28.
GoriPBisegnaF. Thermophysical parameter estimation of multi-layer walls with stochastic optimization methods. Int J Heat Technol2010; 28: 109–116.
TangKSManKFKwnogSHeQ. Genetic algorithms and their applications. IEEE Signal Proc Magn1996; 13: 22–37.
31.
CorcioneMFontanaL. Optimal design of outdoor lighting systems by genetic algorithms. Light Res Technol2003; 35: 261–280.
32.
GuilleminAMorelN. An innovative lighting controller integrated in a self-adaptive building control system. Energ Buildings2001; 33: 477–487.
33.
FerentinosKPAlbrightLD. Optimal design of plant lighting system by genetic algorithms. Eng Appl Artif Intell2005; 18: 473–484.
34.
Ramhadani F, Kamarulnizam AB and Muhammad GS. Optimization of standalone street light system with consideration of lighting control. In: Proceedings of the international conference in technological advances in electrical, electronics and computer engineering (TAEECE), Konya, Turkey, May 9–11 2013.
35.
SeongYBYeoMSKimKW. Optimized control algorithm for automated Venetian blind system considering solar profile variation in buildings. Indoor Built Environ2014; 3: 890–914.
36.
ZhouAQuBYLiHZhaoSZSuganthanPNZhangQ. Multiobjective evolutionary algorithms: a survey on the state of the art. Swarm Evol Comput2011; 1: 32–49.
37.
ZitlerEThieleL. Multiobjective evolutionary algorithms: a comparative case study and the strength Pareto approach. IEEE T Evol Comput1999; 3: 257–271.
38.
Matlab. The MathWorks, Inc., Massachusetts, USA: Natick, 2015.
39.
Goral CM, Torrance KE, Greenberg DP and Battaile B. Modeling the interaction of light between diffuse surfaces. Proceedings of the 11th annual conference on computer graphics and interactive techniques. ACM SIGGRAPH Comput Graph 1984; 18: 213–222.
