Louver configuration comparison in three Canadian cities utilizing NSGA-II

Rafati, Nariman; Hazbei, Morteza; Eicker, Ursula

doi:10.1016/j.buildenv.2022.109939

Cited by 14 publications

(8 citation statements)

References 80 publications

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“…By analyzing some studies of CFD-based genetic algorithms [4,10,26,28,29,60,61] and POD methods [5,6,24,25,62], viewing the source code (such as Geatpy), and communicating with some of the authors, a summary of the three methods (including the method of this paper) was made, as shown in Table 6. Of course, the table analyzed only the simplest form of each method and did not include various types of variants, such as the co-evolution of multiple populations in genetic algorithms, where the gradient dependence comes from the continuity assumption and the interpolation method.…”

Section: Discussionmentioning

confidence: 99%

A New Optimization Design Method of Multi-Objective Indoor Air Supply Using the Kriging Model and NSGA-II

Guo,

Wang,

Cao

et al. 2023

Applied Sciences

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When using meta-heuristic optimization approaches for optimization, a large number of samples are required. In particular, when generating a subgeneration, the utilization of existing samples is low and the number of individuals is high. Therefore, surrogate-based optimization has been developed, which greatly reduces the number of individuals in the subgeneration and the cost of optimization. In complex air supply scenarios, single-objective optimization results may not be comprehensive; therefore, this paper developed a double-objective air supply optimization method based on the Kriging surrogate model and Non-dominated Sorting Genetic Algorithms-II. And it proposed the infill criteria based on clustering to advance the Pareto Frontier. The method was validated with an inverse prediction case, and in particular, the problems when based on 3D steady-state simulations were analyzed. The results showed that the method can quickly achieve an approximate prediction of the boundary conditions (when predictions were made based on experimental data, the number of simulations was 82 and the average error was 6.8%). Finally, the method was used to optimize the air supply parameters of a dual-aisle, single-row cabin, with only 118 samples used in the optimization process. The Pareto set suggested that an airflow organization with dual circulation may be optimal.

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Section: Discussionmentioning

confidence: 99%

A New Optimization Design Method of Multi-Objective Indoor Air Supply Using the Kriging Model and NSGA-II

Guo,

Wang,

Cao

et al. 2023

Applied Sciences

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“…For the individuals with the same dominance level, the selection will be based on crowding degree. More detailed about reservation strategy, congestion sorting process and fast non-dominated sorting can be found in [10]. The development environment is python 3.9.11, the toolkits of Numpy 1.21.0 and Scipy 1.24.0 are used in this work.…”

Section: Overall Optimization Objectivementioning

confidence: 99%

“…By using decision tree and GA-BP algorithm to optimize core magnetic circuit and design magnetic density, no-load loss of distribution transformer can be effectively reduced. Subsequently, many scholars have carried out transformer optimization design according to the above ideas [8][9][10][11]. The above transformer optimization model based on analytical method is more reliable when CMOPS solves the model with high confidence and the algorithm with high accuracy.…”

Section: Introductionmentioning

confidence: 99%

Three-phase Transformer optimization design based on NSGA2 algorithm

Li,

Zhang,

Chen

et al. 2023

J. Phys.: Conf. Ser.

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To solve the problems of lack of diversity and low quality of solution set in the solving process of traditional transformer optimization algorithm, a multi-objective optimization algorithm based on NSGA-II is proposed. Firstly, a multi-objective optimization model of power transformer winding was established with manufacturing cost, additional coil loss and transformer short-circuit impedance as optimization objectives. Secondly, NSGA-II algorithm was used to optimize the model design. The optimization results showed that, compared with traditional MOPSO, MODE and MOEA. NSGA-II algorithm had better fitness values for each target. Finally, the reliability of the scheme is proved in a 110 kV/63000 kVA prototype, which has better economy compared with the traditional scheme.

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“…From 2013 to 2022, GAs have been widely utilized in the analysis of shading devices, [21]. However, as presented in Table 5, the application of GAs in the case of kinetic shading systems is still limited.…”

mentioning

confidence: 99%

“…Propose an integrated, systematic framework that supports kinetic façades' performance design and decision-making from the projects' early design stages MOEA, NSGAII, PAES Not mentioned [21] Evaluate EUI and UDI for the best louver configuration scenario for three Canadian cities R, G, NSGA-II 9:00-17:00 [22] Find the optimal level of the window area with a squared geometry rotational kinematic model with a horizontal axis in the south façade of an office building in Tehran R, G, H, L 8:00-16:00 of 21st of Mar, Jun, and Dec [23] Model and simulate a responsive façade incorporating Miura-ori techniques to enhance visual comfort for building occupants by considering their location and environmental conditions R, G, H, Energy Plus, D, BCMO 9:00, 12:00, 15:00 of 21st of Mar, Jun, and Dec [26] Utilizes an EnergyPlus-based optimization approach to optimize the specifications and control parameters of smart shading blinds, resulting in significant reductions in building energy consumption, discomfort glare index (DGI), and predicted percentage of dissatisfied occupants (PPD) R, G, Energy Plus, NSGA-II Not mentioned * R: Rhinoceros ® ; G: Grasshopper; D: DIVA; H: Honeybee; L: Lady Bug.…”

mentioning

confidence: 99%

Optimizing the Shading Device Configuration of Kinetic Façades through Daylighting Performance Assessment

Kim,

Luong,

Nguyen

2024

Buildings

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When designing a façade, it is essential to consider the impact of daylight and how it can be optimized through external movable shading devices. To accurately evaluate the lighting performance of a kinetic facade, it is crucial to consider the operation of these shading devices, as they can significantly impact performance. This study proposes a high-precision methodology that utilizes digital tools and hourly data to examine the effectiveness of dynamic shading device systems in enhancing daylight performance and optimizing shading configurations using the Genetic Optimization algorithm. The study’s results demonstrate that the proposed methodology is accurate and effective, showing that the optimal operation scenario can exceed LEED v4.1 requirements while meeting daylight availability standards. Designers can achieve optimal performance by adjusting each parameter for a lighting energy-conserving kinetic façade. The limitations and applicability of this method are also discussed.

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Louver configuration comparison in three Canadian cities utilizing NSGA-II

Cited by 14 publications

References 80 publications

A New Optimization Design Method of Multi-Objective Indoor Air Supply Using the Kriging Model and NSGA-II

A New Optimization Design Method of Multi-Objective Indoor Air Supply Using the Kriging Model and NSGA-II

Three-phase Transformer optimization design based on NSGA2 algorithm

Optimizing the Shading Device Configuration of Kinetic Façades through Daylighting Performance Assessment

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