Multi-objective Genetic Algorithm for Interior Lighting Design

Plebe, Alice; Pavone, Mario

doi:10.1007/978-3-319-72926-8_19

Cited by 3 publications

(3 citation statements)

References 21 publications

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“…The case shows that the optimization model has significant advantages, and significant energy savings in the range of 18% to 22% were reported. In 2017, Plebe et al [13] provided a more flexible approach to the interior lighting design by considering E mean , U o , and energy consumption. The solution integrates the 3D graphics software Blender to reproduce architectural spaces and simulate lighting effects by using NSGA-II.…”

Section: Of 18mentioning

confidence: 99%

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Optimization of Indoor Luminaire Layout for General Lighting Scheme Using Improved Particle Swarm Optimization

Sun

2022

Energies

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An improved mathematical model and an improved particle swarm optimization (IPSO) are proposed for the complex design parameters and conflicting design goals of the indoor luminaire layout (ILL) problem. The ILL problem is formulated as a nonlinear constrained mixed-variable optimization problem that has four decision variables. For a general lighting scheme (GLS), the number and location of luminaires can be uniquely determined by optimizing four decision variables, which avoid using program loops to determine the number of luminaires. We improve the particle swarm optimization (PSO) in three aspects: (1) up-down probabilistic rounding (UDPR) method proposed to solve mixed integer, (2) improving the velocity of the best global particle, and (3) using nonlinear inertia weights with random items. The IPSO has better optimization results in an office study compared with the PSO and genetic algorithm (GA). The results are validated by DIALux simulation software, and a maximum deviation of 2.2% is found. The validated results show that the method using four decision variables increased the speed by 10.6% and the success rate by 23.33%. Furthermore, Indoor Luminaire Layout System APP is designed to provide guidelines visually for lighting designers and related researchers.

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Section: Of 18mentioning

confidence: 99%

“…w = w max − T(w max − w min ) T max (13) where w max and w min are the maximum and minimum of inertia weight, T is the current number of iterations, and T max is the number of iterations.…”

Section: Algorithm Design 41 Basic Particle Swarm Algorithmmentioning

confidence: 99%

Optimization of Indoor Luminaire Layout for General Lighting Scheme Using Improved Particle Swarm Optimization

Sun

2022

Energies

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“…Some new approaches use genetic algorithms [11][12][13][14] or neural networks [15] to process knowledge and find a high-quality, close-to-optimal design. However, these methods usually have a problem with computational efficiency: preparing a design for a single street segment takes a few hours, which renders them not applicable in practice.…”

Section: Ai Tools Supporting Large-scale Designmentioning

confidence: 99%

Lighting System Modernization as a Source of Green Energy

et al. 2021

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The use of formal methods and artificial intelligence has made it possible to automatically design outdoor lighting. Quick design for large cities, in a matter of hours instead of weeks, and analysis of various optimization criteria enables to save energy and tune profit stream from lighting retrofit. Since outdoor lighting is of a large scale, having luminaires on every street in urban areas, and since it needs to be retrofitted every 10 to 15 years, choosing proper parameters and light sources leads to significant energy savings. This paper presents the concept and calculations of Levelized Cost of Electricity for outdoor lighting retrofit. It is understood as cost of energy savings, it is in the range from 23.06 to 54.64 EUR/MWh, based on real-world cases. This makes street and road lighting modernization process the best green “energy source” if compared with the 2018 Fraunhofer Institute cost of electricity renewable energy technologies ranking. This indicates that investment in lighting retrofit is more economically and ecologically viable than investment in new renewable energy sources.

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