Application of multi-objective genetic algorithms to interior lighting optimization
| Autor: | Evangelos-Nikolaos D. Madias, Panagiotis A. Kontaxis, Frangiskos V. Topalis |
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| Rok vydání: | 2016 |
| Předmět: |
Engineering
business.industry 020209 energy Mechanical Engineering 0211 other engineering and technologies Sorting Illuminance Control engineering 02 engineering and technology Building and Construction Energy consumption Multi-objective optimization 021105 building & construction Genetic algorithm Metric (mathematics) 0202 electrical engineering electronic engineering information engineering Electrical and Electronic Engineering Smart lighting business Simulation Energy (signal processing) Civil and Structural Engineering |
| Zdroj: | Energy and Buildings. 125:66-74 |
| ISSN: | 0378-7788 |
| DOI: | 10.1016/j.enbuild.2016.04.078 |
| Popis: | The energy consumed by artificial lighting represents a vast amount of total energy consumption of a building. LED luminaires combine many advantages and they are considered a prominent lighting technology. The utilization of miscellaneous optimization methods in lighting control has achieved a variety of benefits, such as energy savings while sustaining illuminance at the required levels. However, there is a lack of methods, which take into account the uniformity of lighting which is a significant factor that should be considered according to the EN 12464. This paper proposes a multi-objective optimization model for artificial lighting control so as to minimize energy consumption and the same time maximize uniformity of lighting while maintaining the illuminance at an appropriate level. Two objective functions have been used, the first is the summation of the dimming levels of the luminaires of an interior space and the second is the coefficient of variation of root mean square error of illuminance which is metric of the uniformity of artificial lighting. Both functions have been formulated as mathematical functions of the dimming levels of the luminaires. Constraints include the required level of illuminance and the luminaires’ dimming capabilities. The Non-Dominated Sorting Genetic Algorithm II is used to carry out the optimization. The proposed model has been implemented in an office room and the results demonstrated significant energy savings up to 22%. The proposed approach is flexible and can be applied to all types of interior spaces and is independent of the geometry and configuration of the room. |
| Databáze: | OpenAIRE |
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