Multi-objective optimization of a nearly zero-energy building based on thermal and visual discomfort minimization using a non-dominated sorting genetic algorithm (NSGA-II)
Multi-objective optimization of a nearly zero-energy building based on thermal and visual discomfort minimization using a non-dominated sorting genetic algorithm (NSGA-II)
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DOI：
10.1016/j.enbuild.2015.06.064
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发表时间：
2015-10-01
影响因子：
6.7
通讯作者：
Pagliano, Lorenzo
中科院分区：
文献类型：
--
作者：
Carlucci, Salvatore;Cattarin, Giulio;Pagliano, Lorenzo
多目标优化方法为建筑物的设计提供了有效的支持。他们的目标是根据不同的和潜在的对比需求确定最有前途的建筑变体。然而，优化主要是用来优化建筑物的能源性能，热舒适性，通常忽略视觉舒适性和室内空气quality.The本研究解决了位于南部意大利的一个独立的净零能源的房子，以尽量减少热和视觉不适的设计。该优化问题包含四个目标函数（冬季和夏季的热不适以及眩光和不适当日光量引起的视觉不适），并使用非支配排序遗传算法（通过Java遗传算法包在GenOpt优化引擎中实现）来指导EnergyPlus仿真引擎，仿真结果是一个四维解集。帕累托边界的建筑变体采用了多样化和非直观的设计方案。为了获得良好的设计实践，还分析了解集的二维投影。最后，在具有许多目标函数的复杂优化问题的情况下，建议使用优化技术来在相对较短的时间内有效地探索大量可用的建筑变体，从而确定可行的非直观解决方案。(C)2015 Elsevier B.V.版权所有。
Multi-objective optimization methods provide a valid support to buildings' design. They aim at identifying the most promising building variants on the basis of diverse and potentially contrasting needs. However, optimization has been mainly used to optimize the energy performance of buildings, giving secondary importance to thermal comfort and usually neglecting visual comfort and the indoor air quality.The present study addresses the design of a detached net zero-energy house located in Southern Italy to minimize thermal and visual discomfort. The optimization problem admits four objective functions (thermal discomfort during winter and summer and visual discomfort due to glare and an inappropriate quantity of daylight) and uses the non-dominated sorting genetic algorithm, implemented in the GenOpt optimization engine through the Java genetic algorithms package, to instruct the EnergyPlus simulation engine.The simulation outcome is a four-dimensional solution set. The building variants of the Pareto frontier adopt diverse and non-intuitive design alternatives. To derive good design practices, two-dimensional projections of the solution set were also analyzed. Finally, in cases of complex optimization problems with many objective functions, optimization techniques are recommended to effectively explore the large number of available building variants in a relatively short time and, hence, identify viable non-intuitive solutions. (C) 2015 Elsevier B.V. All rights reserved.